Keeping employees happy in the company and hiring best match for the specific role is one of the most important aspect of Talent management. People are the backbone of any company. All the resources, infrastructures, and technology provide no value to the company without the topmost talented and skilled employees. Hence, the biggest challenges that talent management company faces every day is not being able to have enough data on attracting and retaining the talented employees. Therefore, our goal in this project is to leverage the power of data science to conduct an analysis on existing employee data to provide some interesting trends that may exists in data set, identify top factors that contribute to turnover and build a model to classify attrition and predict monthly income.
# Question to Address in Analysis:
# Q.1 Identify factors that lead to attrition backed up by evidence provided by analysis.
#Q. 2. Find some specific trends that may exist in the data set (e.g., “Data Scientists have the highest job satisfaction”).
#Q.3. Also provide any other interesting trends and observations from your analysis.
#libraries
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(class)
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
library(modelr)
library(vctrs)
library(qwraps2)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✔ tibble 2.1.3 ✔ purrr 0.3.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
library(DataExplorer) # shows missing values by bar and percentage.
library(openintro)
## Please visit openintro.org for free statistics materials
##
## Attaching package: 'openintro'
## The following object is masked from 'package:caret':
##
## dotPlot
## The following object is masked from 'package:ggplot2':
##
## diamonds
## The following object is masked from 'package:lattice':
##
## lsegments
## The following objects are masked from 'package:datasets':
##
## cars, trees
library(gdata)
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
## The following object is masked from 'package:purrr':
##
## keep
## The following object is masked from 'package:qwraps2':
##
## ll
## The following object is masked from 'package:modelr':
##
## resample
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
## The following object is masked from 'package:stats':
##
## nobs
## The following object is masked from 'package:utils':
##
## object.size
## The following object is masked from 'package:base':
##
## startsWith
library(mapdata)
## Loading required package: maps
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
library(mime)
library(treemap)
library(VIM)
## Loading required package: colorspace
## Loading required package: grid
## Loading required package: data.table
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:gdata':
##
## first, last
## The following object is masked from 'package:purrr':
##
## transpose
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.
## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues
##
## Attaching package: 'VIM'
## The following object is masked from 'package:datasets':
##
## sleep
library(inspectdf)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
##
## Attaching package: 'GGally'
## The following object is masked from 'package:dplyr':
##
## nasa
library(ggthemes)
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:gdata':
##
## combine
## The following object is masked from 'package:dplyr':
##
## combine
library(class)
library(broom)
##
## Attaching package: 'broom'
## The following object is masked from 'package:modelr':
##
## bootstrap
library(caret)
library(MASS)
##
## Attaching package: 'MASS'
## The following objects are masked from 'package:openintro':
##
## housing, mammals
## The following object is masked from 'package:dplyr':
##
## select
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:openintro':
##
## densityPlot
## The following object is masked from 'package:purrr':
##
## some
## The following object is masked from 'package:qwraps2':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
library(cowplot)
##
## ********************************************************
## Note: As of version 1.0.0, cowplot does not change the
## default ggplot2 theme anymore. To recover the previous
## behavior, execute:
## theme_set(theme_cowplot())
## ********************************************************
##
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggthemes':
##
## theme_map
library(corrplot)
## corrplot 0.84 loaded
library(e1071)
library(data.table)
library(dplyr)
library(stats)
library(InformationValue)
##
## Attaching package: 'InformationValue'
## The following objects are masked from 'package:caret':
##
## confusionMatrix, precision, sensitivity, specificity
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:car':
##
## logit
## The following object is masked from 'package:qwraps2':
##
## logit
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
DDSAnalytics <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
head(DDSAnalytics)
## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## 6 10 2 Life Sciences 1 733
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## 6 4 Male 32 3 3
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## 6 Manufacturing Director 1 Divorced 8793
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 9250 2 Y No 11
## 2 17544 1 Y No 14
## 3 19944 2 Y No 11
## 4 24032 1 Y No 19
## 5 17218 1 Y Yes 13
## 6 4809 1 Y No 21
## PerformanceRating RelationshipSatisfaction StandardHours
## 1 3 3 80
## 2 3 1 80
## 3 3 3 80
## 4 3 3 80
## 5 3 3 80
## 6 4 3 80
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 1 1 8 3 2
## 2 0 21 2 4
## 3 0 10 2 3
## 4 2 14 3 3
## 5 0 6 2 3
## 6 2 9 4 2
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 1 5 2 0
## 2 20 7 4
## 3 2 2 2
## 4 14 10 5
## 5 6 3 1
## 6 9 7 1
## YearsWithCurrManager
## 1 3
## 2 9
## 3 2
## 4 7
## 5 3
## 6 7
summary(DDSAnalytics)
## ID Age Attrition BusinessTravel
## Min. : 1.0 Min. :18.00 No :730 Non-Travel : 94
## 1st Qu.:218.2 1st Qu.:30.00 Yes:140 Travel_Frequently:158
## Median :435.5 Median :35.00 Travel_Rarely :618
## Mean :435.5 Mean :36.83
## 3rd Qu.:652.8 3rd Qu.:43.00
## Max. :870.0 Max. :60.00
##
## DailyRate Department DistanceFromHome
## Min. : 103.0 Human Resources : 35 Min. : 1.000
## 1st Qu.: 472.5 Research & Development:562 1st Qu.: 2.000
## Median : 817.5 Sales :273 Median : 7.000
## Mean : 815.2 Mean : 9.339
## 3rd Qu.:1165.8 3rd Qu.:14.000
## Max. :1499.0 Max. :29.000
##
## Education EducationField EmployeeCount EmployeeNumber
## Min. :1.000 Human Resources : 15 Min. :1 Min. : 1.0
## 1st Qu.:2.000 Life Sciences :358 1st Qu.:1 1st Qu.: 477.2
## Median :3.000 Marketing :100 Median :1 Median :1039.0
## Mean :2.901 Medical :270 Mean :1 Mean :1029.8
## 3rd Qu.:4.000 Other : 52 3rd Qu.:1 3rd Qu.:1561.5
## Max. :5.000 Technical Degree: 75 Max. :1 Max. :2064.0
##
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 Female:354 Min. : 30.00 Min. :1.000
## 1st Qu.:2.000 Male :516 1st Qu.: 48.00 1st Qu.:2.000
## Median :3.000 Median : 66.00 Median :3.000
## Mean :2.701 Mean : 65.61 Mean :2.723
## 3rd Qu.:4.000 3rd Qu.: 83.00 3rd Qu.:3.000
## Max. :4.000 Max. :100.00 Max. :4.000
##
## JobLevel JobRole JobSatisfaction
## Min. :1.000 Sales Executive :200 Min. :1.000
## 1st Qu.:1.000 Research Scientist :172 1st Qu.:2.000
## Median :2.000 Laboratory Technician :153 Median :3.000
## Mean :2.039 Manufacturing Director : 87 Mean :2.709
## 3rd Qu.:3.000 Healthcare Representative: 76 3rd Qu.:4.000
## Max. :5.000 Sales Representative : 53 Max. :4.000
## (Other) :129
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## Divorced:191 Min. : 1081 Min. : 2094 Min. :0.000 Y:870
## Married :410 1st Qu.: 2840 1st Qu.: 8092 1st Qu.:1.000
## Single :269 Median : 4946 Median :14074 Median :2.000
## Mean : 6390 Mean :14326 Mean :2.728
## 3rd Qu.: 8182 3rd Qu.:20456 3rd Qu.:4.000
## Max. :19999 Max. :26997 Max. :9.000
##
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## No :618 Min. :11.0 Min. :3.000 Min. :1.000
## Yes:252 1st Qu.:12.0 1st Qu.:3.000 1st Qu.:2.000
## Median :14.0 Median :3.000 Median :3.000
## Mean :15.2 Mean :3.152 Mean :2.707
## 3rd Qu.:18.0 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :25.0 Max. :4.000 Max. :4.000
##
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## Min. :80 Min. :0.0000 Min. : 0.00 Min. :0.000
## 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.00 1st Qu.:2.000
## Median :80 Median :1.0000 Median :10.00 Median :3.000
## Mean :80 Mean :0.7839 Mean :11.05 Mean :2.832
## 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.:15.00 3rd Qu.:3.000
## Max. :80 Max. :3.0000 Max. :40.00 Max. :6.000
##
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :1.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median :3.000 Median : 5.000 Median : 3.000
## Mean :2.782 Mean : 6.962 Mean : 4.205
## 3rd Qu.:3.000 3rd Qu.:10.000 3rd Qu.: 7.000
## Max. :4.000 Max. :40.000 Max. :18.000
##
## YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 2.00
## Median : 1.000 Median : 3.00
## Mean : 2.169 Mean : 4.14
## 3rd Qu.: 3.000 3rd Qu.: 7.00
## Max. :15.000 Max. :17.00
##
colnames(DDSAnalytics)
## [1] "ID" "Age"
## [3] "Attrition" "BusinessTravel"
## [5] "DailyRate" "Department"
## [7] "DistanceFromHome" "Education"
## [9] "EducationField" "EmployeeCount"
## [11] "EmployeeNumber" "EnvironmentSatisfaction"
## [13] "Gender" "HourlyRate"
## [15] "JobInvolvement" "JobLevel"
## [17] "JobRole" "JobSatisfaction"
## [19] "MaritalStatus" "MonthlyIncome"
## [21] "MonthlyRate" "NumCompaniesWorked"
## [23] "Over18" "OverTime"
## [25] "PercentSalaryHike" "PerformanceRating"
## [27] "RelationshipSatisfaction" "StandardHours"
## [29] "StockOptionLevel" "TotalWorkingYears"
## [31] "TrainingTimesLastYear" "WorkLifeBalance"
## [33] "YearsAtCompany" "YearsInCurrentRole"
## [35] "YearsSinceLastPromotion" "YearsWithCurrManager"
dim(DDSAnalytics)
## [1] 870 36
str(DDSAnalytics)
## 'data.frame': 870 obs. of 36 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction: int 3 1 3 3 3 3 1 3 4 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany, y=MonthlyIncome,color=JobRole))+
geom_point()+ggtitle("Year at Company vs. Monthly Income")
# Bar plot of employees by thier Job role.
summary(DDSAnalytics$JobRole)
## Healthcare Representative Human Resources
## 76 27
## Laboratory Technician Manager
## 153 51
## Manufacturing Director Research Director
## 87 51
## Research Scientist Sales Executive
## 172 200
## Sales Representative
## 53
jobRoleTable <- table(DDSAnalytics$JobRole)
jobRoleTableDf <- data.frame(jobRoleTable)
names(jobRoleTableDf) <- c("JobRole","TotalCount")
#What we would like is for R to respect the order in data.frame. For that to happen, we need to change the order of factor levels by specifying the order explicitly.
jobRoleTableDf$JobRole <- factor(jobRoleTableDf$JobRole,levels = jobRoleTableDf$JobRole[order(jobRoleTableDf$TotalCount)])
# library(dplyr)
# jobRoleTableDf%>%ggplot(aes(x=JobRole,y=TotalCount,fill=Jobsatisfaction))+geom_bar(stat = "identity")+coord_flip()+ggtitle("Number of Employe by Job Role")+
# xlab("Job Role")+ylab("Total number of Employees")+theme(
# plot.title = element_text(color = "black",
# hjust = 0.5,
# size = 15),
# legend.position = "none"
# )
DDSAnalytics%>%ggplot(aes(x=Department,fill=Department))+geom_bar()
DDSAnalytics%>%ggplot(aes(x=Education,fill=EducationField))+geom_bar()
DDSAnalytics%>%ggplot(aes(x=JobSatisfaction,fill=EducationField))+geom_bar()
# Job satisfaction is higher for Life Science field.
# Job Satisfaction higher for employees with the job title Sales executive.
# there are very few employee in Job satisfaction level 4 with job title Human Resource.
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=JobSatisfaction,fill=JobRole))+
geom_bar(stat = "identity")
#jobSatisfaction
plyr::count(DDSAnalytics,'JobSatisfaction')->jobSatisfactionFreq
data.frame(jobSatisfactionFreq)
## JobSatisfaction freq
## 1 1 179
## 2 2 166
## 3 3 254
## 4 4 271
jobSatisfactionFreq%>%ggplot(aes(x=JobSatisfaction,y=freq))+
geom_bar(stat = "identity")
# Over all the employee are satisfied with their job.
hist(DDSAnalytics$MonthlyIncome)
hist(DDSAnalytics$MonthlyRate)
hist(DDSAnalytics$YearsAtCompany)
hist(DDSAnalytics$YearsInCurrentRole)
hist(DDSAnalytics$TotalWorkingYears)
hist(DDSAnalytics$DistanceFromHome)
DDSAnalytics%>%ggplot(aes(x=JobSatisfaction,y=YearsAtCompany,color=JobRole))+geom_point()
str(DDSAnalytics$Age)
## int [1:870] 32 40 35 32 24 27 41 37 34 34 ...
DDSAnalytics$Age
## [1] 32 40 35 32 24 27 41 37 34 34 43 28 35 30 46 31 32 46 34 44 36 48 43
## [24] 31 33 44 38 33 53 52 32 33 46 35 32 32 35 36 56 31 58 45 49 42 23 42
## [47] 32 38 18 27 38 58 44 45 31 41 42 29 37 25 36 36 31 18 24 32 35 23 35
## [70] 37 41 28 35 41 52 30 34 46 45 31 37 35 38 37 36 59 47 35 27 46 38 29
## [93] 37 35 50 42 46 37 29 34 27 31 34 23 53 26 29 55 46 32 28 52 52 35 35
## [116] 37 25 29 41 34 36 21 45 38 29 35 32 45 43 46 29 49 42 39 43 40 29 54
## [139] 39 37 56 58 46 37 40 29 33 33 24 28 31 32 32 41 34 30 40 39 43 42 30
## [162] 28 33 42 35 20 49 33 52 29 30 47 46 33 53 21 55 26 32 38 35 38 43 39
## [185] 39 28 25 47 50 28 30 31 46 48 26 51 51 49 44 24 40 32 32 28 32 44 43
## [208] 38 30 41 41 42 37 42 27 40 38 48 30 26 34 45 33 28 38 30 35 22 24 49
## [231] 45 35 47 54 43 37 42 32 25 50 42 45 29 25 29 29 31 32 41 41 33 36 45
## [254] 31 40 43 31 30 34 25 31 29 33 29 24 35 34 32 33 30 45 34 27 36 55 56
## [277] 22 43 25 55 43 41 40 31 38 33 22 34 36 35 45 36 27 47 40 35 34 34 31
## [300] 28 32 35 55 35 58 24 50 34 55 25 34 40 31 51 60 47 34 24 39 19 32 34
## [323] 30 46 39 27 44 42 38 42 49 32 44 27 59 33 35 34 50 27 36 28 33 30 30
## [346] 39 34 26 44 56 42 31 40 40 41 34 26 29 32 36 52 29 52 40 55 56 34 40
## [369] 23 32 31 30 45 37 20 50 36 42 29 30 47 26 48 23 51 38 35 29 36 21 34
## [392] 26 31 60 42 31 46 34 56 44 30 42 47 36 35 43 31 36 53 43 36 41 37 32
## [415] 34 35 30 36 37 29 34 37 31 18 28 43 30 21 32 34 27 40 49 29 40 27 28
## [438] 26 50 38 32 29 28 44 46 47 38 42 38 45 33 44 35 33 48 40 31 51 56 36
## [461] 42 36 50 31 43 38 36 36 26 53 27 29 45 45 28 46 37 26 52 37 29 31 34
## [484] 56 58 41 30 31 48 34 49 50 29 35 36 51 45 37 36 39 58 28 48 29 47 32
## [507] 33 51 37 41 38 39 32 33 21 51 25 42 30 56 34 28 45 49 59 53 45 31 35
## [530] 56 38 36 29 32 29 43 38 42 24 42 30 33 27 38 34 34 49 40 38 45 41 28
## [553] 36 48 40 37 54 34 42 26 36 58 27 31 30 44 32 30 42 50 41 21 28 34 26
## [576] 37 38 44 39 29 25 42 38 28 44 40 38 36 37 35 22 34 28 49 40 33 35 40
## [599] 27 24 24 32 30 29 29 53 59 38 26 33 35 41 33 19 30 34 33 44 39 35 20
## [622] 47 34 35 28 27 24 38 55 34 55 34 38 36 43 35 42 34 24 27 30 40 55 34
## [645] 41 40 18 26 35 40 35 60 40 48 22 26 51 54 41 36 32 34 46 35 34 29 29
## [668] 33 32 54 37 33 28 35 27 46 36 40 36 31 25 43 36 57 28 31 29 33 19 38
## [691] 38 47 42 30 37 47 48 36 18 46 29 34 35 26 59 41 54 31 38 50 30 57 48
## [714] 36 36 24 30 28 35 28 37 37 34 36 42 25 37 34 26 22 43 39 27 32 34 27
## [737] 39 59 35 35 25 31 37 23 25 24 29 34 24 33 20 31 50 46 54 44 35 49 38
## [760] 45 29 47 41 50 34 45 41 40 46 45 33 46 39 36 42 23 30 32 26 29 41 39
## [783] 33 33 47 31 51 52 55 25 49 34 38 36 49 33 45 22 19 36 33 31 39 35 44
## [806] 36 25 22 30 27 30 25 24 47 38 33 30 33 30 28 32 29 34 31 36 46 31 34
## [829] 38 50 45 19 28 19 55 37 37 34 47 48 18 29 52 34 34 28 19 39 40 26 31
## [852] 30 29 28 35 35 30 31 41 44 51 43 34 47 45 48 32 47 45 35
summary(DDSAnalytics$Age)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 30.00 35.00 36.83 43.00 60.00
hist(DDSAnalytics$Age)
#employee training programs, identifying high-potential employees and reducing/preventing voluntary employee turnover (attrition).
as.factor(DDSAnalytics$TrainingTimesLastYear)->traingTimeFactor
as.factor(DDSAnalytics$JobInvolvement)->jobInvFactor
dfTrainTimJobInv <- data.frame(JobInv=DDSAnalytics$JobInvolvement,
Training=traingTimeFactor,
EmployeeJobSatisfaction=DDSAnalytics$JobSatisfaction,
jobInvFac= jobInvFactor)
#dfTrainTimJobInv%>%ggplot(aes(x=JobInv))+geom_bar(aes(fill=Training))
library(GGally)
dfTrainTimJobInv%>%dplyr::select(JobInv,Training,EmployeeJobSatisfaction)%>%
ggpairs(aes(color=Training))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dfTrainTimJobInv%>%ggplot(aes(Training))+geom_bar(aes(fill=jobInvFac)) ->df1
#EDA, Correlation and Variable Selection
#Correlation
DDSAnalytics%>%dplyr::select(Age, DailyRate,DistanceFromHome,
Education, EnvironmentSatisfaction,
HourlyRate,JobInvolvement,JobLevel,
JobSatisfaction, MonthlyIncome,MonthlyRate,
NumCompaniesWorked,PerformanceRating,TotalWorkingYears,
RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
WorkLifeBalance,YearsInCurrentRole, YearsSinceLastPromotion,
YearsAtCompany, YearsWithCurrManager,
PercentSalaryHike,HourlyRate)->corVarSelection
#cor(k,method = "pearson")
#corrplot(DDSAnalytics, method = "number")
#k2 <- rcorr(as.matrix(k))
library(corrplot)
cor_selected_Var <-cor(corVarSelection)
head(round(cor_selected_Var,2))
## Age DailyRate DistanceFromHome Education
## Age 1.00 0.01 0.01 0.22
## DailyRate 0.01 1.00 0.01 -0.01
## DistanceFromHome 0.01 0.01 1.00 0.05
## Education 0.22 -0.01 0.05 1.00
## EnvironmentSatisfaction -0.01 -0.01 -0.04 -0.04
## HourlyRate 0.05 0.05 0.07 0.01
## EnvironmentSatisfaction HourlyRate JobInvolvement
## Age -0.01 0.05 0.02
## DailyRate -0.01 0.05 0.06
## DistanceFromHome -0.04 0.07 0.00
## Education -0.04 0.01 0.03
## EnvironmentSatisfaction 1.00 -0.03 0.00
## HourlyRate -0.03 1.00 0.07
## JobLevel JobSatisfaction MonthlyIncome MonthlyRate
## Age 0.48 -0.02 0.48 0.07
## DailyRate 0.00 0.00 0.00 -0.03
## DistanceFromHome 0.02 -0.02 -0.01 -0.01
## Education 0.13 0.01 0.13 -0.02
## EnvironmentSatisfaction 0.00 -0.02 -0.02 0.06
## HourlyRate -0.01 -0.09 0.00 -0.02
## NumCompaniesWorked PerformanceRating
## Age 0.29 -0.04
## DailyRate 0.05 -0.02
## DistanceFromHome -0.05 0.03
## Education 0.16 -0.03
## EnvironmentSatisfaction 0.01 0.00
## HourlyRate 0.01 0.00
## TotalWorkingYears RelationshipSatisfaction
## Age 0.65 -0.01
## DailyRate -0.01 0.01
## DistanceFromHome 0.00 0.04
## Education 0.15 -0.03
## EnvironmentSatisfaction -0.02 0.00
## HourlyRate 0.03 0.02
## StockOptionLevel TrainingTimesLastYear
## Age 0.04 -0.05
## DailyRate 0.02 -0.01
## DistanceFromHome 0.07 -0.04
## Education 0.03 -0.06
## EnvironmentSatisfaction 0.03 -0.01
## HourlyRate 0.06 0.01
## WorkLifeBalance YearsInCurrentRole
## Age -0.01 0.21
## DailyRate -0.03 0.00
## DistanceFromHome -0.01 -0.01
## Education 0.01 0.06
## EnvironmentSatisfaction 0.08 0.02
## HourlyRate -0.03 0.00
## YearsSinceLastPromotion YearsAtCompany
## Age 0.22 0.29
## DailyRate -0.06 -0.04
## DistanceFromHome -0.02 -0.02
## Education 0.07 0.06
## EnvironmentSatisfaction 0.01 -0.02
## HourlyRate 0.01 0.00
## YearsWithCurrManager PercentSalaryHike
## Age 0.19 -0.03
## DailyRate -0.02 0.03
## DistanceFromHome -0.02 0.05
## Education 0.09 0.00
## EnvironmentSatisfaction -0.02 0.00
## HourlyRate 0.00 -0.02
corrplot(cor_selected_Var,method = "circle")
corrplot(cor_selected_Var,method = "number") # correlation check for all variables
# Displays diagnoal histogram
library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'xts'
## The following objects are masked from 'package:data.table':
##
## first, last
## The following objects are masked from 'package:gdata':
##
## first, last
## The following objects are masked from 'package:dplyr':
##
## first, last
##
## Attaching package: 'PerformanceAnalytics'
## The following objects are masked from 'package:e1071':
##
## kurtosis, skewness
## The following object is masked from 'package:graphics':
##
## legend
chart.Correlation(cor_selected_Var,histogram = TRUE, pac=19)
DDSAnalytics%>%ggplot(aes(x=Age,y=TotalWorkingYears,color=Gender))+geom_point()
DDSAnalytics%>%ggplot(aes(x=JobLevel,y=MonthlyIncome,color=Department))+geom_point()
DDSAnalytics%>%ggplot(aes(x=JobLevel,y=TotalWorkingYears))+geom_point()
DDSAnalytics%>%ggplot(aes(x=TotalWorkingYears,y=MonthlyIncome,color=Gender))+geom_point()
DDSAnalytics%>%ggplot(aes(x=YearsInCurrentRole,y=YearsWithCurrManager,color=Gender))+geom_point()
DDSAnalytics%>%ggplot(aes(x=YearsSinceLastPromotion,y=YearsAtCompany,color=Gender))+geom_point()
DDSAnalytics%>%dplyr::select(Age,MonthlyIncome,TotalWorkingYears,PercentSalaryHike,YearsWithCurrManager,
YearsInCurrentRole,YearsSinceLastPromotion,MonthlyRate,NumCompaniesWorked,
RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
WorkLifeBalance,YearsAtCompany)->selected_var
selected_var_correlation <- cor(selected_var)
head(round(selected_var_correlation,2))
## Age MonthlyIncome TotalWorkingYears
## Age 1.00 0.48 0.65
## MonthlyIncome 0.48 1.00 0.78
## TotalWorkingYears 0.65 0.78 1.00
## PercentSalaryHike -0.03 -0.05 -0.06
## YearsWithCurrManager 0.19 0.33 0.46
## YearsInCurrentRole 0.21 0.36 0.49
## PercentSalaryHike YearsWithCurrManager
## Age -0.03 0.19
## MonthlyIncome -0.05 0.33
## TotalWorkingYears -0.06 0.46
## PercentSalaryHike 1.00 -0.05
## YearsWithCurrManager -0.05 1.00
## YearsInCurrentRole -0.02 0.71
## YearsInCurrentRole YearsSinceLastPromotion
## Age 0.21 0.22
## MonthlyIncome 0.36 0.32
## TotalWorkingYears 0.49 0.45
## PercentSalaryHike -0.02 -0.07
## YearsWithCurrManager 0.71 0.51
## YearsInCurrentRole 1.00 0.55
## MonthlyRate NumCompaniesWorked
## Age 0.07 0.29
## MonthlyIncome 0.06 0.16
## TotalWorkingYears 0.06 0.26
## PercentSalaryHike 0.00 -0.02
## YearsWithCurrManager -0.02 -0.12
## YearsInCurrentRole 0.03 -0.10
## RelationshipSatisfaction StockOptionLevel
## Age -0.01 0.04
## MonthlyIncome 0.00 0.02
## TotalWorkingYears -0.02 0.04
## PercentSalaryHike -0.05 0.00
## YearsWithCurrManager -0.02 0.04
## YearsInCurrentRole 0.00 0.08
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Age -0.05 -0.01 0.29
## MonthlyIncome -0.04 0.02 0.49
## TotalWorkingYears -0.04 0.02 0.64
## PercentSalaryHike 0.00 0.01 -0.06
## YearsWithCurrManager 0.00 0.02 0.77
## YearsInCurrentRole -0.02 0.08 0.78
corrplot(selected_var_correlation,method = "circle")
corrplot(selected_var_correlation,method = "number")
# selected variables with no correlation
DDSAnalytics%>%dplyr::select(Age,PercentSalaryHike,MonthlyRate,NumCompaniesWorked,
RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
WorkLifeBalance,YearsAtCompany,DistanceFromHome,
Education,JobInvolvement,HourlyRate)->selected_var_correlation1
# selected_var_correlation1$MonthlyIncome_log = log(selected_var_correlation1$MonthlyIncome)
# selected_var_correlation1$MonthlyRate_log = log(selected_var_correlation1$MonthlyRate) # log made it left swequed.
selected_var_correlation1_cor <- cor(selected_var_correlation1)
head(round(selected_var_correlation1_cor,2))
## Age PercentSalaryHike MonthlyRate
## Age 1.00 -0.03 0.07
## PercentSalaryHike -0.03 1.00 0.00
## MonthlyRate 0.07 0.00 1.00
## NumCompaniesWorked 0.29 -0.02 0.02
## RelationshipSatisfaction -0.01 -0.05 -0.02
## StockOptionLevel 0.04 0.00 -0.04
## NumCompaniesWorked RelationshipSatisfaction
## Age 0.29 -0.01
## PercentSalaryHike -0.02 -0.05
## MonthlyRate 0.02 -0.02
## NumCompaniesWorked 1.00 0.04
## RelationshipSatisfaction 0.04 1.00
## StockOptionLevel 0.03 -0.03
## StockOptionLevel TrainingTimesLastYear
## Age 0.04 -0.05
## PercentSalaryHike 0.00 0.00
## MonthlyRate -0.04 -0.01
## NumCompaniesWorked 0.03 -0.07
## RelationshipSatisfaction -0.03 0.02
## StockOptionLevel 1.00 0.02
## WorkLifeBalance YearsAtCompany DistanceFromHome
## Age -0.01 0.29 0.01
## PercentSalaryHike 0.01 -0.06 0.05
## MonthlyRate 0.01 -0.02 -0.01
## NumCompaniesWorked 0.02 -0.14 -0.05
## RelationshipSatisfaction 0.04 0.01 0.04
## StockOptionLevel 0.05 0.03 0.07
## Education JobInvolvement HourlyRate
## Age 0.22 0.02 0.05
## PercentSalaryHike 0.00 0.01 -0.02
## MonthlyRate -0.02 -0.02 -0.02
## NumCompaniesWorked 0.16 -0.01 0.01
## RelationshipSatisfaction -0.03 0.02 0.02
## StockOptionLevel 0.03 0.07 0.06
corrplot(selected_var_correlation1_cor,method = "number")
chart.Correlation(selected_var_correlation1_cor,histogram = TRUE, pac=19)
library(ggpubr)
## Loading required package: magrittr
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:cowplot':
##
## get_legend
## The following objects are masked from 'package:qwraps2':
##
## mean_ci, mean_sd, median_iqr
#Variable Selection and Model Fitting
library(MASS)
#DDSAnalytics <- read.csv(file.choose())
# Fit the model
fitMod <- lm(MonthlyIncome~Age+DailyRate+PercentSalaryHike+MonthlyRate+NumCompaniesWorked+
RelationshipSatisfaction+StockOptionLevel+TrainingTimesLastYear+
WorkLifeBalance+YearsAtCompany+DistanceFromHome+
Education+JobInvolvement+HourlyRate,data = DDSAnalytics)
# Stepwise variable selection
#Variables selected are: Age MonthlyRate NumCompaniesWorked YearsAtCompany
modStepw <- stepAIC(fitMod,direction = "both")
## Start: AIC=14279.99
## MonthlyIncome ~ Age + DailyRate + PercentSalaryHike + MonthlyRate +
## NumCompaniesWorked + RelationshipSatisfaction + StockOptionLevel +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## DistanceFromHome + Education + JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - DailyRate 1 1063345 1.1298e+10 14278
## - DistanceFromHome 1 1120616 1.1298e+10 14278
## - RelationshipSatisfaction 1 2121077 1.1299e+10 14278
## - Education 1 2140131 1.1299e+10 14278
## - WorkLifeBalance 1 2206959 1.1299e+10 14278
## - StockOptionLevel 1 2264705 1.1299e+10 14278
## - JobInvolvement 1 4092665 1.1301e+10 14278
## - HourlyRate 1 4993530 1.1302e+10 14278
## - PercentSalaryHike 1 6789217 1.1304e+10 14278
## - TrainingTimesLastYear 1 8966200 1.1306e+10 14279
## <none> 1.1297e+10 14280
## - MonthlyRate 1 39077410 1.1336e+10 14281
## - NumCompaniesWorked 1 206505811 1.1503e+10 14294
## - Age 1 1483997780 1.2781e+10 14385
## - YearsAtCompany 1 2679215399 1.3976e+10 14463
##
## Step: AIC=14278.08
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## RelationshipSatisfaction + StockOptionLevel + TrainingTimesLastYear +
## WorkLifeBalance + YearsAtCompany + DistanceFromHome + Education +
## JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - DistanceFromHome 1 1145851 1.1299e+10 14276
## - Education 1 2064590 1.1300e+10 14276
## - RelationshipSatisfaction 1 2086685 1.1300e+10 14276
## - WorkLifeBalance 1 2117587 1.1300e+10 14276
## - StockOptionLevel 1 2214071 1.1300e+10 14276
## - JobInvolvement 1 4337161 1.1302e+10 14276
## - HourlyRate 1 4814904 1.1303e+10 14276
## - PercentSalaryHike 1 6644296 1.1305e+10 14277
## - TrainingTimesLastYear 1 9007046 1.1307e+10 14277
## <none> 1.1298e+10 14278
## - MonthlyRate 1 38752514 1.1337e+10 14279
## + DailyRate 1 1063345 1.1297e+10 14280
## - NumCompaniesWorked 1 208285019 1.1506e+10 14292
## - Age 1 1484842711 1.2783e+10 14384
## - YearsAtCompany 1 2678231646 1.3976e+10 14461
##
## Step: AIC=14276.16
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## RelationshipSatisfaction + StockOptionLevel + TrainingTimesLastYear +
## WorkLifeBalance + YearsAtCompany + Education + JobInvolvement +
## HourlyRate
##
## Df Sum of Sq RSS AIC
## - RelationshipSatisfaction 1 1947945 1.1301e+10 14274
## - StockOptionLevel 1 1997929 1.1301e+10 14274
## - WorkLifeBalance 1 2073908 1.1301e+10 14274
## - Education 1 2244098 1.1301e+10 14274
## - JobInvolvement 1 4246236 1.1303e+10 14274
## - HourlyRate 1 4538008 1.1304e+10 14274
## - PercentSalaryHike 1 6359489 1.1305e+10 14275
## - TrainingTimesLastYear 1 9294642 1.1308e+10 14275
## <none> 1.1299e+10 14276
## - MonthlyRate 1 38700725 1.1338e+10 14277
## + DistanceFromHome 1 1145851 1.1298e+10 14278
## + DailyRate 1 1088580 1.1298e+10 14278
## - NumCompaniesWorked 1 207140743 1.1506e+10 14290
## - Age 1 1487126021 1.2786e+10 14382
## - YearsAtCompany 1 2677897927 1.3977e+10 14459
##
## Step: AIC=14274.31
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## StockOptionLevel + TrainingTimesLastYear + WorkLifeBalance +
## YearsAtCompany + Education + JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - StockOptionLevel 1 1848939 1.1303e+10 14272
## - WorkLifeBalance 1 1928217 1.1303e+10 14272
## - Education 1 2376371 1.1303e+10 14272
## - JobInvolvement 1 4126458 1.1305e+10 14273
## - HourlyRate 1 4636501 1.1306e+10 14273
## - PercentSalaryHike 1 6063184 1.1307e+10 14273
## - TrainingTimesLastYear 1 9437706 1.1311e+10 14273
## <none> 1.1301e+10 14274
## - MonthlyRate 1 39151712 1.1340e+10 14275
## + RelationshipSatisfaction 1 1947945 1.1299e+10 14276
## + DailyRate 1 1053348 1.1300e+10 14276
## + DistanceFromHome 1 1007112 1.1300e+10 14276
## - NumCompaniesWorked 1 205607025 1.1507e+10 14288
## - Age 1 1489380826 1.2790e+10 14380
## - YearsAtCompany 1 2676081706 1.3977e+10 14457
##
## Step: AIC=14272.46
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## Education + JobInvolvement + HourlyRate
##
## Df Sum of Sq RSS AIC
## - WorkLifeBalance 1 1759117 1.1305e+10 14271
## - Education 1 2308802 1.1305e+10 14271
## - JobInvolvement 1 3777372 1.1307e+10 14271
## - HourlyRate 1 5002592 1.1308e+10 14271
## - PercentSalaryHike 1 6049458 1.1309e+10 14271
## - TrainingTimesLastYear 1 9615796 1.1313e+10 14271
## <none> 1.1303e+10 14272
## - MonthlyRate 1 39818844 1.1343e+10 14274
## + StockOptionLevel 1 1848939 1.1301e+10 14274
## + RelationshipSatisfaction 1 1798956 1.1301e+10 14274
## + DailyRate 1 1006075 1.1302e+10 14274
## + DistanceFromHome 1 817380 1.1302e+10 14274
## - NumCompaniesWorked 1 204777164 1.1508e+10 14286
## - Age 1 1487744081 1.2791e+10 14378
## - YearsAtCompany 1 2674278411 1.3977e+10 14455
##
## Step: AIC=14270.59
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany + Education + JobInvolvement +
## HourlyRate
##
## Df Sum of Sq RSS AIC
## - Education 1 2349298 1.1307e+10 14269
## - JobInvolvement 1 3842262 1.1309e+10 14269
## - HourlyRate 1 5153532 1.1310e+10 14269
## - PercentSalaryHike 1 5990888 1.1311e+10 14269
## - TrainingTimesLastYear 1 9432510 1.1314e+10 14269
## <none> 1.1305e+10 14271
## - MonthlyRate 1 39984889 1.1345e+10 14272
## + WorkLifeBalance 1 1759117 1.1303e+10 14272
## + StockOptionLevel 1 1679840 1.1303e+10 14272
## + RelationshipSatisfaction 1 1671684 1.1303e+10 14272
## + DailyRate 1 929403 1.1304e+10 14272
## + DistanceFromHome 1 795656 1.1304e+10 14272
## - NumCompaniesWorked 1 206424162 1.1511e+10 14284
## - Age 1 1485987397 1.2791e+10 14376
## - YearsAtCompany 1 2684987116 1.3990e+10 14454
##
## Step: AIC=14268.77
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany + JobInvolvement +
## HourlyRate
##
## Df Sum of Sq RSS AIC
## - JobInvolvement 1 4032699 1.1311e+10 14267
## - HourlyRate 1 5198903 1.1312e+10 14267
## - PercentSalaryHike 1 5939200 1.1313e+10 14267
## - TrainingTimesLastYear 1 9923213 1.1317e+10 14268
## <none> 1.1307e+10 14269
## - MonthlyRate 1 39351114 1.1346e+10 14270
## + Education 1 2349298 1.1305e+10 14271
## + WorkLifeBalance 1 1799613 1.1305e+10 14271
## + RelationshipSatisfaction 1 1794779 1.1305e+10 14271
## + StockOptionLevel 1 1613162 1.1305e+10 14271
## + DistanceFromHome 1 949046 1.1306e+10 14271
## + DailyRate 1 854948 1.1306e+10 14271
## - NumCompaniesWorked 1 213428005 1.1520e+10 14283
## - Age 1 1544879419 1.2852e+10 14378
## - YearsAtCompany 1 2691269215 1.3998e+10 14452
##
## Step: AIC=14267.08
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany + HourlyRate
##
## Df Sum of Sq RSS AIC
## - HourlyRate 1 4608935 1.1316e+10 14265
## - PercentSalaryHike 1 5810127 1.1317e+10 14266
## - TrainingTimesLastYear 1 10211076 1.1321e+10 14266
## <none> 1.1311e+10 14267
## - MonthlyRate 1 38847658 1.1350e+10 14268
## + JobInvolvement 1 4032699 1.1307e+10 14269
## + Education 1 2539735 1.1309e+10 14269
## + WorkLifeBalance 1 1868554 1.1309e+10 14269
## + RelationshipSatisfaction 1 1697863 1.1309e+10 14269
## + StockOptionLevel 1 1273880 1.1310e+10 14269
## + DailyRate 1 1068020 1.1310e+10 14269
## + DistanceFromHome 1 896886 1.1310e+10 14269
## - NumCompaniesWorked 1 212128122 1.1523e+10 14281
## - Age 1 1551816172 1.2863e+10 14377
## - YearsAtCompany 1 2687657896 1.3999e+10 14451
##
## Step: AIC=14265.44
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked +
## TrainingTimesLastYear + YearsAtCompany
##
## Df Sum of Sq RSS AIC
## - PercentSalaryHike 1 5586325 1.1321e+10 14264
## - TrainingTimesLastYear 1 10326155 1.1326e+10 14264
## <none> 1.1316e+10 14265
## - MonthlyRate 1 39414999 1.1355e+10 14266
## + HourlyRate 1 4608935 1.1311e+10 14267
## + JobInvolvement 1 3442732 1.1312e+10 14267
## + Education 1 2569654 1.1313e+10 14267
## + WorkLifeBalance 1 2010778 1.1314e+10 14267
## + RelationshipSatisfaction 1 1780010 1.1314e+10 14267
## + StockOptionLevel 1 1586224 1.1314e+10 14267
## + DailyRate 1 864891 1.1315e+10 14267
## + DistanceFromHome 1 639252 1.1315e+10 14267
## - NumCompaniesWorked 1 212501684 1.1528e+10 14280
## - Age 1 1547207287 1.2863e+10 14375
## - YearsAtCompany 1 2692826523 1.4009e+10 14449
##
## Step: AIC=14263.87
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + TrainingTimesLastYear +
## YearsAtCompany
##
## Df Sum of Sq RSS AIC
## - TrainingTimesLastYear 1 10333102 1.1332e+10 14263
## <none> 1.1321e+10 14264
## - MonthlyRate 1 39373688 1.1361e+10 14265
## + PercentSalaryHike 1 5586325 1.1316e+10 14265
## + HourlyRate 1 4385133 1.1317e+10 14266
## + JobInvolvement 1 3339411 1.1318e+10 14266
## + Education 1 2513386 1.1319e+10 14266
## + WorkLifeBalance 1 1945145 1.1319e+10 14266
## + StockOptionLevel 1 1574609 1.1320e+10 14266
## + RelationshipSatisfaction 1 1509312 1.1320e+10 14266
## + DailyRate 1 748081 1.1321e+10 14266
## + DistanceFromHome 1 456620 1.1321e+10 14266
## - NumCompaniesWorked 1 214556393 1.1536e+10 14278
## - Age 1 1548022772 1.2869e+10 14373
## - YearsAtCompany 1 2715028200 1.4036e+10 14449
##
## Step: AIC=14262.66
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + YearsAtCompany
##
## Df Sum of Sq RSS AIC
## <none> 1.1332e+10 14263
## - MonthlyRate 1 39763836 1.1371e+10 14264
## + TrainingTimesLastYear 1 10333102 1.1321e+10 14264
## + PercentSalaryHike 1 5593272 1.1326e+10 14264
## + HourlyRate 1 4497282 1.1327e+10 14264
## + JobInvolvement 1 3595979 1.1328e+10 14264
## + Education 1 3038550 1.1329e+10 14264
## + WorkLifeBalance 1 1751465 1.1330e+10 14264
## + StockOptionLevel 1 1738488 1.1330e+10 14264
## + RelationshipSatisfaction 1 1651462 1.1330e+10 14264
## + DailyRate 1 785720 1.1331e+10 14265
## + DistanceFromHome 1 653090 1.1331e+10 14265
## - NumCompaniesWorked 1 220217707 1.1552e+10 14277
## - Age 1 1557187987 1.2889e+10 14373
## - YearsAtCompany 1 2709372536 1.4041e+10 14447
modStepw$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## MonthlyIncome ~ Age + DailyRate + PercentSalaryHike + MonthlyRate +
## NumCompaniesWorked + RelationshipSatisfaction + StockOptionLevel +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## DistanceFromHome + Education + JobInvolvement + HourlyRate
##
## Final Model:
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + YearsAtCompany
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 855 11296930706 14279.99
## 2 - DailyRate 1 1063345 856 11297994051 14278.08
## 3 - DistanceFromHome 1 1145851 857 11299139902 14276.16
## 4 - RelationshipSatisfaction 1 1947945 858 11301087847 14274.31
## 5 - StockOptionLevel 1 1848939 859 11302936786 14272.46
## 6 - WorkLifeBalance 1 1759117 860 11304695904 14270.59
## 7 - Education 1 2349298 861 11307045202 14268.77
## 8 - JobInvolvement 1 4032699 862 11311077901 14267.08
## 9 - HourlyRate 1 4608935 863 11315686836 14265.44
## 10 - PercentSalaryHike 1 5586325 864 11321273161 14263.87
## 11 - TrainingTimesLastYear 1 10333102 865 11331606263 14262.66
# histogram and qqplot of residuals
hist(modStepw$residuals,col="orange",main = "Residual Histogram")
ggqqplot(modStepw$residuals,color = "orange",title = "Residual Normality Check") # qqplot
plot(modStepw$residuals,col="orange",main = "Residual Plots")
#Cross Validation
# Spliting dataset into Training and Test and Cross Validation
set.seed(5)
ddsObs = sample(seq(1,dim(DDSAnalytics)[1]),round(.75*dim(DDSAnalytics)[1]),replace = FALSE)
ddsTrain = DDSAnalytics[ddsObs,]
ddsTrain
## ID Age Attrition BusinessTravel DailyRate Department
## 834 834 19 No Travel_Rarely 645 Research & Development
## 697 697 48 No Travel_Frequently 365 Research & Development
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 715 715 36 No Non-Travel 1434 Sales
## 809 809 30 No Travel_Rarely 793 Research & Development
## 725 725 42 No Travel_Rarely 1142 Research & Development
## 222 222 45 Yes Travel_Frequently 306 Sales
## 71 71 41 No Travel_Rarely 642 Research & Development
## 503 503 48 Yes Travel_Frequently 708 Sales
## 403 403 47 No Travel_Rarely 207 Research & Development
## 387 387 35 No Travel_Frequently 944 Sales
## 806 806 36 No Travel_Rarely 1383 Research & Development
## 826 826 46 No Travel_Rarely 430 Research & Development
## 821 821 32 No Travel_Rarely 634 Research & Development
## 844 844 34 No Travel_Rarely 943 Research & Development
## 528 528 31 No Travel_Rarely 1332 Research & Development
## 128 128 45 No Travel_Rarely 556 Research & Development
## 539 539 24 Yes Travel_Rarely 1448 Sales
## 457 457 31 No Travel_Rarely 1112 Sales
## 732 732 39 Yes Travel_Rarely 1162 Sales
## 842 842 29 No Travel_Rarely 332 Human Resources
## 85 85 36 No Travel_Rarely 429 Research & Development
## 122 122 21 No Travel_Rarely 501 Sales
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 819 819 30 No Travel_Rarely 1427 Research & Development
## 625 625 28 Yes Travel_Frequently 289 Research & Development
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 92 92 29 Yes Travel_Rarely 408 Sales
## 269 269 33 No Travel_Frequently 1303 Research & Development
## 463 463 50 No Travel_Frequently 809 Sales
## 407 407 31 No Travel_Rarely 196 Sales
## 235 235 43 No Travel_Frequently 394 Sales
## 54 54 45 No Non-Travel 805 Research & Development
## 146 146 29 No Non-Travel 746 Sales
## 464 464 31 No Travel_Frequently 163 Research & Development
## 192 192 31 No Travel_Frequently 1125 Sales
## 665 665 34 No Non-Travel 1065 Sales
## 394 394 60 No Travel_Frequently 1499 Sales
## 810 810 27 No Travel_Rarely 1157 Research & Development
## 512 512 39 No Non-Travel 1485 Research & Development
## 628 628 38 No Travel_Rarely 343 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 8 8 37 No Travel_Rarely 309 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 851 851 31 Yes Travel_Rarely 542 Sales
## 404 404 36 No Travel_Rarely 711 Research & Development
## 365 365 55 No Travel_Rarely 282 Research & Development
## 491 491 49 No Travel_Frequently 636 Research & Development
## 616 616 34 No Travel_Rarely 1157 Research & Development
## 748 748 34 No Travel_Frequently 669 Research & Development
## 123 123 45 No Travel_Rarely 930 Sales
## 841 841 18 Yes Travel_Rarely 230 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 149 149 24 No Travel_Rarely 477 Research & Development
## 91 91 38 No Travel_Frequently 1490 Research & Development
## 72 72 28 No Travel_Rarely 866 Sales
## 563 563 27 No Travel_Rarely 1134 Research & Development
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 828 828 34 No Travel_Rarely 131 Sales
## 481 481 29 No Travel_Rarely 136 Research & Development
## 466 466 38 No Travel_Rarely 371 Research & Development
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
## 381 381 47 No Travel_Rarely 1482 Research & Development
## 90 90 46 No Travel_Rarely 705 Sales
## 127 127 32 Yes Travel_Frequently 1125 Research & Development
## 737 737 39 No Travel_Rarely 722 Sales
## 527 527 45 No Travel_Rarely 1268 Sales
## 100 100 34 No Travel_Rarely 401 Research & Development
## 209 209 30 No Travel_Rarely 201 Research & Development
## 148 148 33 No Travel_Rarely 1242 Sales
## 125 125 29 No Travel_Frequently 442 Sales
## 754 754 46 Yes Travel_Rarely 1254 Sales
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 649 649 35 No Travel_Rarely 776 Sales
## 516 516 51 No Travel_Rarely 1302 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 412 412 41 No Travel_Rarely 918 Sales
## 667 667 29 No Travel_Frequently 1413 Sales
## 227 227 35 No Travel_Rarely 464 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 27 27 38 No Travel_Rarely 1245 Sales
## 240 240 50 No Travel_Rarely 813 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 422 422 37 No Non-Travel 1063 Research & Development
## 130 130 46 No Travel_Rarely 1319 Sales
## 784 784 33 No Travel_Frequently 1146 Sales
## 188 188 47 No Travel_Rarely 1225 Sales
## 521 521 34 No Travel_Frequently 702 Research & Development
## 745 745 25 No Travel_Rarely 1382 Sales
## 256 256 43 No Non-Travel 343 Research & Development
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 262 262 29 Yes Travel_Rarely 224 Research & Development
## 369 369 23 No Travel_Rarely 977 Research & Development
## 573 573 28 No Travel_Frequently 783 Sales
## 135 135 43 No Travel_Frequently 559 Research & Development
## 152 152 32 No Travel_Rarely 929 Sales
## 374 374 37 No Travel_Rarely 1470 Research & Development
## 822 822 29 No Travel_Rarely 1247 Sales
## 785 785 47 No Non-Travel 543 Sales
## 446 446 47 No Travel_Rarely 465 Research & Development
## 793 793 38 No Travel_Rarely 397 Research & Development
## 17 17 32 No Travel_Rarely 498 Research & Development
## 718 718 28 No Travel_Rarely 857 Research & Development
## 176 176 21 No Travel_Rarely 984 Research & Development
## 319 319 39 No Travel_Rarely 1498 Sales
## 372 372 30 No Non-Travel 1398 Sales
## 461 461 42 No Travel_Frequently 1474 Research & Development
## 361 361 52 No Travel_Rarely 699 Research & Development
## 141 141 56 No Travel_Rarely 1369 Research & Development
## 28 28 33 Yes Travel_Rarely 603 Sales
## 423 423 31 Yes Travel_Rarely 249 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 796 796 33 Yes Travel_Frequently 1076 Research & Development
## 859 859 41 No Travel_Rarely 933 Research & Development
## 295 295 40 No Travel_Rarely 898 Human Resources
## 574 574 34 No Travel_Rarely 810 Sales
## 703 703 35 No Non-Travel 1212 Sales
## 334 334 27 No Travel_Rarely 608 Research & Development
## 268 268 32 Yes Travel_Rarely 1259 Research & Development
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 671 671 37 No Travel_Rarely 397 Research & Development
## 51 51 38 No Travel_Rarely 362 Research & Development
## 153 153 32 No Travel_Rarely 906 Sales
## 441 441 32 No Travel_Frequently 1316 Research & Development
## 154 154 41 No Travel_Rarely 447 Research & Development
## 87 87 47 No Travel_Rarely 703 Sales
## 575 575 26 No Travel_Rarely 703 Sales
## 414 414 32 No Travel_Frequently 1318 Sales
## 763 763 41 No Non-Travel 256 Sales
## 58 58 29 No Travel_Rarely 1370 Research & Development
## 224 224 28 No Travel_Rarely 1423 Research & Development
## 580 580 29 No Travel_Rarely 1329 Research & Development
## 622 622 47 No Travel_Frequently 1379 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 847 847 19 No Travel_Rarely 1181 Research & Development
## 542 542 33 No Travel_Rarely 867 Research & Development
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 67 67 35 No Travel_Rarely 1017 Research & Development
## 25 25 33 No Travel_Rarely 1069 Research & Development
## 288 288 34 No Travel_Rarely 1346 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
## 827 827 31 Yes Travel_Rarely 1079 Sales
## 728 728 34 No Travel_Frequently 878 Research & Development
## 730 730 22 No Travel_Rarely 594 Research & Development
## 303 303 55 No Travel_Rarely 1441 Research & Development
## 69 69 35 No Travel_Frequently 664 Research & Development
## 264 264 29 No Travel_Frequently 1404 Sales
## 562 562 58 No Travel_Frequently 1216 Research & Development
## 675 675 27 No Travel_Rarely 486 Research & Development
## 427 427 30 No Non-Travel 990 Research & Development
## 223 223 33 No Non-Travel 530 Sales
## 35 35 32 No Travel_Rarely 1373 Research & Development
## 533 533 29 No Travel_Rarely 1252 Research & Development
## 239 239 25 No Travel_Rarely 309 Human Resources
## 243 243 29 No Travel_Rarely 1328 Research & Development
## 707 707 54 No Travel_Rarely 431 Research & Development
## 43 43 49 No Travel_Rarely 464 Research & Development
## 777 777 30 No Travel_Rarely 330 Human Resources
## 419 419 37 No Travel_Rarely 977 Research & Development
## 333 333 44 Yes Travel_Frequently 429 Research & Development
## 640 640 27 Yes Travel_Rarely 1420 Sales
## 757 757 35 No Travel_Rarely 1137 Research & Development
## 774 774 36 Yes Travel_Rarely 318 Research & Development
## 864 864 47 No Travel_Frequently 217 Sales
## 499 499 36 No Travel_Rarely 1299 Research & Development
## 484 484 56 No Travel_Rarely 713 Research & Development
## 281 281 43 No Travel_Rarely 1179 Sales
## 84 84 37 No Travel_Rarely 1319 Research & Development
## 761 761 29 Yes Travel_Rarely 906 Research & Development
## 37 37 35 Yes Travel_Rarely 737 Sales
## 292 292 36 No Non-Travel 1105 Research & Development
## 537 537 38 No Travel_Rarely 130 Sales
## 118 118 29 No Travel_Rarely 1378 Research & Development
## 261 261 31 Yes Travel_Frequently 703 Sales
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 459 459 56 No Travel_Rarely 1255 Research & Development
## 593 593 28 Yes Travel_Frequently 1496 Sales
## 448 448 42 No Travel_Rarely 1128 Research & Development
## 26 26 44 No Travel_Rarely 1099 Sales
## 599 599 27 No Travel_Rarely 155 Research & Development
## 113 113 52 No Travel_Rarely 994 Research & Development
## 345 345 30 No Travel_Rarely 1329 Sales
## 870 870 35 No Travel_Frequently 138 Research & Development
## 177 177 55 Yes Travel_Rarely 725 Research & Development
## 156 156 30 No Non-Travel 641 Sales
## 568 568 30 Yes Travel_Rarely 740 Sales
## 673 673 28 No Travel_Frequently 467 Sales
## 432 432 40 No Non-Travel 458 Research & Development
## 759 759 38 No Travel_Rarely 1495 Research & Development
## 440 440 38 No Travel_Rarely 827 Research & Development
## 347 347 34 No Travel_Frequently 1003 Research & Development
## 833 833 28 No Travel_Rarely 1179 Research & Development
## 274 274 36 No Travel_Rarely 329 Sales
## 40 40 31 No Travel_Frequently 793 Sales
## 636 636 35 No Travel_Frequently 636 Research & Development
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 216 216 40 No Travel_Rarely 896 Research & Development
## 312 312 40 No Travel_Rarely 302 Research & Development
## 305 305 58 Yes Travel_Rarely 289 Research & Development
## 838 838 34 No Travel_Rarely 258 Sales
## 686 686 31 No Travel_Rarely 1274 Research & Development
## 162 162 28 Yes Non-Travel 1366 Research & Development
## 866 866 48 No Travel_Rarely 855 Research & Development
## 832 832 19 Yes Travel_Rarely 528 Sales
## 627 627 24 No Travel_Rarely 1476 Sales
## 329 329 38 No Travel_Frequently 1394 Research & Development
## 179 179 32 No Travel_Frequently 967 Sales
## 545 545 34 No Travel_Rarely 121 Research & Development
## 397 397 46 No Travel_Rarely 1009 Research & Development
## 185 185 39 No Travel_Frequently 443 Research & Development
## 357 357 26 No Travel_Rarely 841 Research & Development
## 170 170 29 No Travel_Rarely 1107 Research & Development
## 251 251 33 No Non-Travel 1313 Research & Development
## 638 638 34 No Travel_Rarely 1239 Sales
## 869 869 45 No Travel_Rarely 1457 Research & Development
## 429 429 32 Yes Travel_Rarely 515 Research & Development
## 57 57 42 Yes Travel_Frequently 933 Research & Development
## 705 705 59 No Travel_Rarely 818 Human Resources
## 55 55 31 No Travel_Rarely 746 Research & Development
## 853 853 29 No Travel_Rarely 153 Research & Development
## 478 478 26 No Travel_Rarely 775 Sales
## 797 797 45 No Travel_Rarely 1385 Research & Development
## 770 770 45 No Non-Travel 1050 Sales
## 257 257 31 No Travel_Frequently 444 Sales
## 79 79 45 No Non-Travel 1052 Sales
## 66 66 32 No Travel_Rarely 334 Research & Development
## 687 687 29 Yes Travel_Frequently 337 Research & Development
## 339 339 50 Yes Travel_Frequently 959 Sales
## 631 631 55 No Travel_Rarely 836 Research & Development
## 676 676 46 No Travel_Rarely 168 Sales
## 143 143 46 No Travel_Rarely 566 Research & Development
## 561 561 36 No Travel_Frequently 1480 Research & Development
## 254 254 31 Yes Travel_Rarely 359 Human Resources
## 189 189 50 No Travel_Rarely 797 Research & Development
## 1 1 32 No Travel_Rarely 117 Sales
## 858 858 31 No Travel_Frequently 798 Research & Development
## 578 578 44 No Non-Travel 981 Research & Development
## 234 234 54 No Travel_Rarely 1147 Sales
## 751 751 20 Yes Travel_Frequently 769 Sales
## 299 299 31 Yes Non-Travel 335 Research & Development
## 543 543 27 No Non-Travel 1450 Research & Development
## 214 214 42 No Travel_Rarely 932 Research & Development
## 366 366 56 No Travel_Frequently 906 Sales
## 624 624 35 No Travel_Rarely 1146 Human Resources
## 610 610 33 No Non-Travel 1038 Sales
## 250 250 41 No Travel_Rarely 1276 Sales
## 119 119 41 No Travel_Rarely 465 Research & Development
## 103 103 34 No Non-Travel 1381 Sales
## 221 221 34 No Travel_Rarely 304 Sales
## 630 630 34 No Travel_Rarely 1442 Research & Development
## 605 605 29 Yes Travel_Rarely 428 Sales
## 462 462 36 No Travel_Rarely 311 Research & Development
## 714 714 36 No Travel_Frequently 541 Sales
## 517 517 25 Yes Travel_Rarely 867 Sales
## 287 287 22 Yes Travel_Rarely 391 Research & Development
## 519 519 30 No Travel_Rarely 1138 Research & Development
## 674 674 35 Yes Travel_Frequently 130 Research & Development
## 392 392 26 No Travel_Rarely 1443 Sales
## 758 758 49 No Travel_Rarely 1495 Research & Development
## 514 514 33 No Travel_Frequently 515 Research & Development
## 668 668 33 Yes Travel_Rarely 587 Research & Development
## 317 317 34 No Travel_Frequently 303 Sales
## 52 52 58 No Travel_Rarely 1055 Research & Development
## 511 511 38 No Travel_Rarely 345 Sales
## 815 815 38 No Travel_Rarely 395 Sales
## 341 341 36 No Travel_Rarely 852 Research & Development
## 612 612 41 No Non-Travel 509 Research & Development
## 564 564 31 No Non-Travel 697 Research & Development
## 183 183 43 No Travel_Frequently 957 Research & Development
## 97 97 46 No Travel_Rarely 228 Sales
## 602 602 32 No Non-Travel 1200 Research & Development
## 711 711 30 No Travel_Rarely 634 Research & Development
## 586 586 40 No Travel_Rarely 905 Research & Development
## 642 642 40 No Travel_Rarely 750 Research & Development
## 651 651 35 No Travel_Rarely 992 Research & Development
## 666 666 29 No Travel_Frequently 461 Research & Development
## 508 508 51 No Travel_Frequently 541 Sales
## 70 70 37 No Travel_Rarely 1372 Research & Development
## 73 73 35 Yes Travel_Rarely 556 Research & Development
## 509 509 37 No Travel_Frequently 319 Sales
## 704 704 26 Yes Travel_Rarely 920 Human Resources
## 455 455 48 No Travel_Rarely 1236 Research & Development
## 662 662 34 No Travel_Rarely 1303 Research & Development
## 7 7 41 No Travel_Rarely 1283 Research & Development
## 198 198 49 No Travel_Rarely 174 Sales
## 442 442 29 No Travel_Rarely 1082 Research & Development
## 756 756 44 No Non-Travel 381 Research & Development
## 322 322 34 No Travel_Rarely 1397 Research & Development
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 150 150 28 No Travel_Rarely 580 Research & Development
## 232 232 35 No Travel_Rarely 890 Sales
## 421 421 34 No Travel_Rarely 1153 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 786 786 31 No Travel_Rarely 182 Research & Development
## 241 241 42 No Travel_Rarely 319 Research & Development
## 228 228 22 No Travel_Rarely 1136 Research & Development
## 284 284 31 No Travel_Rarely 691 Sales
## 99 99 29 No Travel_Frequently 410 Research & Development
## 181 181 35 No Travel_Rarely 819 Research & Development
## 496 496 51 No Travel_Rarely 1318 Sales
## 39 39 56 No Travel_Rarely 206 Human Resources
## 212 212 42 No Travel_Rarely 265 Sales
## 107 107 29 No Non-Travel 1496 Research & Development
## 325 325 39 No Travel_Rarely 466 Research & Development
## 635 635 43 No Travel_Rarely 1001 Research & Development
## 343 343 33 No Travel_Rarely 654 Research & Development
## 237 237 42 No Travel_Rarely 300 Research & Development
## 206 206 44 No Travel_Rarely 661 Research & Development
## 607 607 59 No Travel_Frequently 1225 Sales
## 318 318 24 No Travel_Rarely 771 Research & Development
## 772 772 46 No Travel_Rarely 526 Sales
## 645 645 41 Yes Non-Travel 906 Research & Development
## 749 749 24 No Travel_Rarely 506 Research & Development
## 385 385 51 Yes Travel_Rarely 1323 Research & Development
## 794 794 36 No Travel_Rarely 1223 Research & Development
## 313 313 31 Yes Travel_Frequently 561 Research & Development
## 380 380 30 Yes Travel_Rarely 138 Research & Development
## 260 260 25 No Travel_Rarely 977 Research & Development
## 10 10 34 No Travel_Frequently 653 Research & Development
## 447 447 38 No Travel_Frequently 1189 Research & Development
## 534 534 32 No Non-Travel 1146 Research & Development
## 497 497 45 No Travel_Rarely 192 Research & Development
## 352 352 31 No Travel_Rarely 218 Sales
## 746 746 24 No Travel_Rarely 350 Research & Development
## 158 158 39 Yes Travel_Frequently 203 Research & Development
## 483 483 34 No Travel_Frequently 735 Research & Development
## 323 323 30 No Travel_Frequently 1012 Research & Development
## 324 324 46 No Travel_Rarely 1003 Research & Development
## 535 535 29 Yes Travel_Rarely 318 Research & Development
## 307 307 50 No Travel_Rarely 804 Research & Development
## 694 694 30 Yes Travel_Frequently 464 Research & Development
## 750 750 33 No Travel_Rarely 134 Research & Development
## 16 16 31 No Non-Travel 1188 Sales
## 855 855 35 No Travel_Rarely 219 Research & Development
## 823 823 34 No Travel_Rarely 704 Sales
## 591 591 22 No Non-Travel 457 Research & Development
## 360 360 36 No Travel_Rarely 1403 Research & Development
## 470 470 53 No Travel_Rarely 1084 Research & Development
## 68 68 23 No Travel_Rarely 571 Research & Development
## 548 548 40 No Travel_Frequently 720 Research & Development
## 670 670 54 No Travel_Rarely 685 Research & Development
## 424 424 18 No Non-Travel 1124 Research & Development
## 121 121 36 No Non-Travel 427 Research & Development
## 818 818 33 No Travel_Rarely 213 Research & Development
## 258 258 30 No Travel_Rarely 855 Sales
## 696 696 47 No Travel_Rarely 1454 Sales
## 812 812 25 No Travel_Rarely 949 Research & Development
## 479 479 52 No Travel_Rarely 258 Research & Development
## 490 490 34 No Travel_Rarely 970 Research & Development
## 867 867 32 No Non-Travel 976 Sales
## 471 471 27 No Travel_Rarely 1354 Research & Development
## 862 862 43 No Travel_Rarely 531 Sales
## 824 824 31 Yes Travel_Frequently 874 Research & Development
## 522 522 28 Yes Travel_Rarely 329 Research & Development
## 401 401 30 No Travel_Rarely 317 Research & Development
## 353 353 40 No Travel_Frequently 902 Research & Development
## 163 163 33 No Travel_Rarely 1216 Sales
## 492 492 50 No Travel_Rarely 1322 Research & Development
## 820 820 28 Yes Travel_Rarely 1475 Sales
## 632 632 34 No Travel_Rarely 937 Sales
## 384 384 23 No Travel_Rarely 541 Sales
## 644 644 34 Yes Non-Travel 967 Research & Development
## 792 792 34 No Travel_Rarely 629 Research & Development
## 525 525 59 No Travel_Rarely 142 Research & Development
## 677 677 36 No Travel_Rarely 676 Research & Development
## 807 807 25 No Travel_Rarely 1372 Sales
## 825 825 36 No Travel_Rarely 559 Research & Development
## 551 551 41 No Travel_Rarely 314 Human Resources
## 328 328 42 No Travel_Rarely 557 Research & Development
## 356 356 34 No Travel_Rarely 1326 Sales
## 77 77 34 No Travel_Rarely 971 Sales
## 116 116 37 No Non-Travel 1252 Sales
## 450 450 45 No Non-Travel 336 Sales
## 701 701 29 No Travel_Rarely 352 Human Resources
## 443 443 28 Yes Travel_Rarely 529 Research & Development
## 739 739 35 No Travel_Rarely 1219 Sales
## 835 835 55 No Non-Travel 177 Research & Development
## 160 160 42 No Travel_Frequently 458 Research & Development
## 850 850 26 No Travel_Frequently 1096 Research & Development
## 604 604 29 No Travel_Rarely 1401 Research & Development
## 165 165 35 No Travel_Rarely 1232 Sales
## 606 606 53 No Travel_Rarely 970 Research & Development
## 524 524 49 No Travel_Rarely 1245 Research & Development
## 126 126 35 No Travel_Rarely 1224 Sales
## 93 93 37 Yes Travel_Rarely 1373 Research & Development
## 418 418 36 No Travel_Frequently 566 Research & Development
## 64 64 18 No Non-Travel 1431 Research & Development
## 685 685 28 No Travel_Rarely 440 Research & Development
## 293 293 27 No Travel_Rarely 894 Research & Development
## 727 727 37 No Travel_Frequently 1231 Sales
## 729 729 26 Yes Travel_Rarely 471 Research & Development
## 860 860 44 Yes Travel_Rarely 621 Research & Development
## 609 609 26 No Travel_Rarely 652 Research & Development
## 246 246 29 No Travel_Rarely 1086 Research & Development
## 375 375 20 No Travel_Rarely 727 Sales
## 159 159 43 No Travel_Frequently 1082 Research & Development
## 108 108 55 No Travel_Rarely 189 Human Resources
## 489 489 48 Yes Travel_Rarely 626 Research & Development
## 391 391 34 No Travel_Rarely 1111 Sales
## 82 82 35 No Travel_Rarely 1370 Research & Development
## 379 379 29 No Travel_Rarely 942 Research & Development
## 439 439 50 Yes Travel_Rarely 869 Sales
## 681 681 25 No Travel_Rarely 583 Sales
## 477 477 37 No Travel_Rarely 342 Sales
## 314 314 51 No Travel_Rarely 1169 Research & Development
## 843 843 52 No Travel_Frequently 890 Research & Development
## 863 863 34 Yes Non-Travel 1362 Sales
## 839 839 47 No Travel_Rarely 1001 Research & Development
## 362 362 29 Yes Travel_Frequently 746 Sales
## 660 660 36 No Travel_Frequently 1195 Research & Development
## 643 643 55 No Non-Travel 444 Research & Development
## 310 310 25 No Travel_Rarely 810 Sales
## 45 45 23 No Travel_Rarely 373 Research & Development
## 390 390 21 Yes Travel_Frequently 756 Sales
## 193 193 46 No Travel_Rarely 1485 Research & Development
## 494 494 35 No Travel_Rarely 1029 Research & Development
## 34 34 35 No Travel_Rarely 982 Research & Development
## 78 78 46 No Travel_Rarely 563 Sales
## 344 344 30 No Travel_Frequently 160 Research & Development
## 753 753 50 No Travel_Frequently 1234 Research & Development
## 202 202 32 No Travel_Rarely 548 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## 36 36 32 No Travel_Frequently 1311 Research & Development
## 849 849 40 Yes Travel_Rarely 575 Sales
## 340 340 27 No Travel_Rarely 1240 Research & Development
## 132 132 49 No Travel_Rarely 1313 Sales
## 373 373 45 No Travel_Frequently 1249 Research & Development
## 747 747 29 Yes Travel_Rarely 350 Human Resources
## 44 44 42 No Travel_Rarely 1059 Research & Development
## 267 267 34 No Travel_Frequently 829 Research & Development
## 410 410 43 No Travel_Rarely 589 Research & Development
## 760 760 45 Yes Travel_Rarely 1449 Sales
## 788 788 52 Yes Travel_Rarely 723 Research & Development
## 596 596 33 No Travel_Rarely 586 Sales
## 302 302 35 No Travel_Frequently 853 Sales
## 731 731 43 No Travel_Rarely 177 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
## 454 454 33 No Travel_Rarely 217 Sales
## 382 382 26 No Travel_Rarely 1355 Human Resources
## 647 647 18 Yes Non-Travel 247 Research & Development
## 445 445 46 No Travel_Rarely 734 Research & Development
## 804 804 35 Yes Travel_Rarely 1204 Sales
## 104 104 23 No Travel_Rarely 885 Research & Development
## 437 437 28 Yes Travel_Rarely 890 Research & Development
## 656 656 26 No Travel_Frequently 496 Research & Development
## 679 679 36 No Travel_Frequently 688 Research & Development
## 752 752 31 Yes Travel_Frequently 1060 Sales
## 426 426 43 No Travel_Rarely 415 Sales
## 296 296 35 No Travel_Rarely 1490 Research & Development
## 420 420 29 No Travel_Rarely 986 Research & Development
## 830 830 50 No Travel_Frequently 1246 Human Resources
## 32 32 33 No Travel_Rarely 267 Research & Development
## 726 726 25 No Travel_Rarely 685 Research & Development
## 166 166 20 Yes Travel_Rarely 500 Sales
## 247 247 31 No Travel_Rarely 1232 Research & Development
## 117 117 25 No Travel_Rarely 1356 Sales
## 845 845 34 No Travel_Rarely 511 Sales
## 364 364 40 No Travel_Rarely 1416 Research & Development
## 557 557 54 No Travel_Rarely 548 Research & Development
## 376 376 50 No Travel_Frequently 333 Research & Development
## 98 98 37 No Non-Travel 1040 Research & Development
## 502 502 28 Yes Travel_Rarely 103 Research & Development
## 736 736 27 No Travel_Rarely 511 Sales
## 180 180 38 No Travel_Rarely 702 Sales
## 338 338 34 No Travel_Frequently 618 Research & Development
## 501 501 58 Yes Travel_Rarely 601 Research & Development
## 778 778 32 No Non-Travel 862 Sales
## 531 531 38 No Travel_Rarely 330 Research & Development
## 744 744 23 No Travel_Rarely 160 Research & Development
## 196 196 51 No Travel_Rarely 833 Research & Development
## 30 30 52 No Non-Travel 715 Research & Development
## 31 31 32 No Travel_Frequently 689 Sales
## 400 400 44 No Travel_Frequently 1193 Research & Development
## 95 95 50 No Travel_Rarely 1452 Research & Development
## 49 49 18 Yes Travel_Frequently 1306 Sales
## 771 771 33 No Travel_Frequently 508 Sales
## 659 659 41 No Travel_Rarely 483 Research & Development
## 465 465 43 Yes Travel_Frequently 807 Research & Development
## 698 698 36 No Travel_Rarely 1396 Research & Development
## 798 798 22 Yes Travel_Rarely 1294 Research & Development
## 782 782 39 No Travel_Rarely 408 Research & Development
## 291 291 45 No Travel_Rarely 974 Research & Development
## 764 764 50 No Non-Travel 145 Sales
## 211 211 41 No Travel_Rarely 582 Research & Development
## 634 634 36 No Travel_Rarely 922 Research & Development
## 654 654 48 No Travel_Rarely 530 Sales
## 775 775 42 No Travel_Frequently 288 Research & Development
## 559 559 42 No Travel_Rarely 933 Research & Development
## 805 805 44 No Non-Travel 489 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 110 110 32 No Travel_Rarely 529 Research & Development
## 857 857 30 Yes Travel_Rarely 1005 Research & Development
## 337 337 35 No Travel_Frequently 1182 Sales
## 115 115 35 No Travel_Rarely 1361 Sales
## 585 585 44 No Travel_Rarely 1459 Research & Development
## 460 460 36 No Travel_Rarely 172 Research & Development
## 505 505 47 No Travel_Rarely 359 Research & Development
## 576 576 37 No Travel_Rarely 1115 Research & Development
## 799 799 19 Yes Non-Travel 504 Research & Development
## 856 856 35 No Travel_Rarely 660 Sales
## 233 233 47 Yes Travel_Frequently 1093 Sales
## 780 780 29 Yes Travel_Rarely 1092 Research & Development
## 129 129 43 No Travel_Rarely 930 Research & Development
## 837 837 37 No Travel_Rarely 1439 Research & Development
## 766 766 45 No Travel_Rarely 1015 Research & Development
## 569 569 42 No Travel_Rarely 462 Sales
## 388 388 29 No Travel_Rarely 232 Research & Development
## 321 321 32 No Non-Travel 1184 Research & Development
## 308 308 34 No Non-Travel 697 Research & Development
## 829 829 38 No Travel_Frequently 594 Research & Development
## 94 94 35 No Travel_Rarely 670 Research & Development
## 289 289 36 No Travel_Frequently 1467 Sales
## 408 408 36 No Travel_Rarely 917 Research & Development
## 368 368 40 No Travel_Rarely 1342 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 413 413 37 No Travel_Rarely 408 Research & Development
## 487 487 30 Yes Travel_Rarely 945 Sales
## 767 767 41 No Travel_Rarely 509 Research & Development
## 733 733 27 Yes Travel_Frequently 1337 Human Resources
## 735 735 34 No Travel_Rarely 479 Research & Development
## 808 808 22 No Travel_Rarely 1256 Research & Development
## 458 458 51 No Travel_Rarely 632 Sales
## 552 552 28 No Travel_Rarely 1169 Human Resources
## 53 53 44 No Travel_Rarely 1117 Research & Development
## 409 409 53 No Travel_Rarely 1219 Sales
## 702 702 34 No Travel_Rarely 1400 Sales
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 327 327 44 No Travel_Rarely 1313 Research & Development
## 415 415 34 Yes Travel_Frequently 296 Sales
## 83 83 38 No Travel_Frequently 508 Research & Development
## 396 396 31 No Travel_Rarely 192 Research & Development
## 133 133 42 No Travel_Frequently 1368 Research & Development
## 275 275 55 No Travel_Rarely 692 Research & Development
## 456 456 40 No Travel_Rarely 611 Sales
## 518 518 42 No Travel_Rarely 855 Research & Development
## 304 304 35 No Travel_Rarely 1395 Research & Development
## 238 238 32 No Travel_Rarely 646 Research & Development
## 507 507 33 Yes Travel_Rarely 350 Sales
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 434 434 29 No Travel_Rarely 590 Research & Development
## 495 495 36 No Travel_Rarely 1266 Sales
## 724 724 36 No Travel_Rarely 796 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 147 147 33 No Travel_Rarely 931 Research & Development
## 331 331 49 No Travel_Rarely 809 Research & Development
## 218 218 48 No Travel_Frequently 117 Research & Development
## 555 555 40 No Non-Travel 1151 Research & Development
## 201 201 40 Yes Non-Travel 1479 Sales
## 587 587 38 No Non-Travel 573 Research & Development
## 290 290 35 No Travel_Rarely 384 Sales
## 467 467 36 No Travel_Rarely 1157 Sales
## 393 393 31 No Non-Travel 976 Research & Development
## 255 255 40 No Non-Travel 218 Research & Development
## 558 558 34 No Travel_Rarely 254 Research & Development
## 300 300 28 Yes Travel_Rarely 1157 Research & Development
## 253 253 45 No Travel_Rarely 252 Research & Development
## 592 592 34 Yes Travel_Rarely 790 Sales
## 59 59 37 No Non-Travel 728 Research & Development
## 811 811 30 No Non-Travel 111 Research & Development
## 278 278 43 No Travel_Frequently 185 Research & Development
## 137 137 29 Yes Travel_Rarely 805 Research & Development
## 225 225 38 No Travel_Rarely 1206 Research & Development
## 191 191 30 No Travel_Rarely 1275 Research & Development
## 544 544 38 No Travel_Rarely 1261 Research & Development
## 473 473 45 No Travel_Rarely 193 Research & Development
## 523 523 45 No Travel_Rarely 1339 Research & Development
## 710 710 50 No Non-Travel 881 Research & Development
## 861 861 51 No Travel_Frequently 968 Research & Development
## 182 182 38 No Travel_Rarely 1009 Sales
## 852 852 30 No Travel_Rarely 125 Research & Development
## 236 236 37 Yes Travel_Rarely 625 Sales
## 779 779 26 No Non-Travel 786 Research & Development
## 187 187 25 No Travel_Frequently 772 Research & Development
## 717 717 30 No Travel_Rarely 852 Sales
## 740 740 35 No Travel_Rarely 538 Research & Development
## 629 629 55 No Travel_Frequently 1091 Research & Development
## 311 311 34 No Travel_Rarely 1351 Research & Development
## 399 399 56 No Non-Travel 667 Research & Development
## 131 131 29 No Travel_Rarely 991 Sales
## 486 486 41 Yes Travel_Rarely 1102 Sales
## 370 370 32 No Travel_Rarely 1018 Research & Development
## 199 199 44 No Travel_Rarely 477 Research & Development
## 783 783 33 No Travel_Rarely 1075 Human Resources
## 566 566 44 No Travel_Rarely 1037 Research & Development
## 226 226 30 No Travel_Rarely 911 Research & Development
## 488 488 31 No Travel_Rarely 670 Research & Development
## 614 614 19 Yes Travel_Rarely 419 Sales
## 320 320 19 No Travel_Rarely 265 Research & Development
## 800 800 36 No Travel_Rarely 1040 Research & Development
## 700 700 46 No Travel_Rarely 1450 Research & Development
## 762 762 47 No Travel_Rarely 202 Research & Development
## 684 684 57 No Travel_Rarely 334 Research & Development
## 220 220 26 No Travel_Rarely 583 Research & Development
## 229 229 24 No Travel_Rarely 1353 Sales
## 11 11 43 No Travel_Rarely 823 Research & Development
## 342 342 28 No Travel_Frequently 193 Research & Development
## 716 716 24 Yes Travel_Rarely 240 Human Resources
## 65 65 24 Yes Travel_Rarely 984 Research & Development
## 259 259 34 No Travel_Rarely 1480 Sales
## 540 540 42 No Travel_Rarely 647 Sales
## 24 24 31 No Travel_Frequently 715 Sales
## 134 134 39 No Travel_Rarely 492 Research & Development
## 590 590 35 No Travel_Rarely 1142 Research & Development
## 286 286 33 No Travel_Rarely 589 Research & Development
## 12 12 28 No Non-Travel 280 Human Resources
## 249 249 41 No Travel_Frequently 1200 Research & Development
## 529 529 35 No Travel_Rarely 1214 Research & Development
## 215 215 27 No Travel_Rarely 1115 Research & Development
## 449 449 38 No Travel_Frequently 653 Research & Development
## 20 20 44 No Travel_Rarely 170 Research & Development
## 530 530 56 Yes Travel_Rarely 441 Research & Development
## 294 294 47 No Non-Travel 1162 Research & Development
## 89 89 27 No Travel_Frequently 994 Sales
## 435 435 40 No Travel_Rarely 658 Sales
## 436 436 27 No Travel_Rarely 1130 Sales
## 330 330 42 No Travel_Rarely 1147 Human Resources
## 639 639 24 No Travel_Rarely 691 Research & Development
## 663 663 46 No Travel_Rarely 1277 Sales
## 469 469 26 Yes Non-Travel 265 Sales
## 868 868 47 No Travel_Rarely 571 Sales
## 713 713 48 No Travel_Rarely 969 Research & Development
## 594 594 49 No Travel_Rarely 722 Research & Development
## 579 579 39 No Travel_Frequently 711 Research & Development
## 646 646 40 No Travel_Rarely 630 Sales
## 633 633 38 No Travel_Rarely 1035 Sales
## 581 581 25 No Travel_Rarely 882 Research & Development
## 513 513 32 No Travel_Frequently 585 Research & Development
## 619 619 39 No Travel_Rarely 613 Research & Development
## 282 282 41 No Travel_Rarely 263 Research & Development
## 791 791 49 No Travel_Rarely 271 Research & Development
## 699 699 18 Yes Travel_Frequently 544 Sales
## 601 601 24 No Non-Travel 830 Sales
## 210 210 41 No Travel_Rarely 802 Sales
## 172 172 47 No Non-Travel 1169 Research & Development
## 22 22 48 No Travel_Rarely 817 Sales
## 453 453 35 Yes Travel_Rarely 303 Sales
## 776 776 23 No Travel_Rarely 310 Research & Development
## 626 626 27 No Travel_Rarely 1167 Research & Development
## 252 252 36 No Non-Travel 1229 Sales
## 480 480 37 No Travel_Rarely 161 Research & Development
## 648 648 26 No Travel_Rarely 390 Research & Development
## 840 840 48 No Travel_Rarely 1224 Research & Development
## 814 814 47 No Travel_Rarely 1176 Human Resources
## 285 285 38 No Travel_Rarely 433 Human Resources
## 438 438 26 No Travel_Rarely 157 Research & Development
## 279 279 25 Yes Travel_Rarely 240 Sales
## 203 203 32 No Travel_Rarely 859 Research & Development
## 405 405 35 No Travel_Rarely 1343 Research & Development
## 138 138 54 No Travel_Rarely 1441 Research & Development
## 217 217 38 No Travel_Rarely 1495 Research & Development
## 854 854 28 No Travel_Rarely 1476 Research & Development
## DistanceFromHome Education EducationField EmployeeCount
## 834 9 2 Life Sciences 1
## 697 4 5 Medical 1
## 207 9 3 Marketing 1
## 715 8 4 Life Sciences 1
## 809 16 1 Life Sciences 1
## 725 8 3 Life Sciences 1
## 222 26 4 Life Sciences 1
## 71 1 3 Life Sciences 1
## 503 7 2 Medical 1
## 403 9 4 Life Sciences 1
## 387 1 3 Marketing 1
## 806 10 3 Life Sciences 1
## 826 1 4 Medical 1
## 821 5 4 Other 1
## 844 9 3 Life Sciences 1
## 528 11 2 Medical 1
## 128 25 2 Life Sciences 1
## 539 1 1 Technical Degree 1
## 457 5 4 Life Sciences 1
## 732 3 2 Medical 1
## 842 17 3 Other 1
## 85 2 4 Life Sciences 1
## 122 5 1 Medical 1
## 474 29 3 Medical 1
## 819 2 1 Medical 1
## 625 2 2 Medical 1
## 184 6 3 Life Sciences 1
## 92 23 1 Life Sciences 1
## 269 7 2 Life Sciences 1
## 463 12 3 Marketing 1
## 407 29 4 Marketing 1
## 235 26 2 Life Sciences 1
## 54 4 2 Life Sciences 1
## 146 2 3 Life Sciences 1
## 464 24 1 Technical Degree 1
## 192 7 4 Marketing 1
## 665 23 4 Marketing 1
## 394 28 3 Marketing 1
## 810 17 3 Technical Degree 1
## 512 25 2 Life Sciences 1
## 628 15 2 Life Sciences 1
## 248 1 4 Medical 1
## 8 10 4 Life Sciences 1
## 468 6 3 Life Sciences 1
## 851 20 3 Life Sciences 1
## 404 5 4 Life Sciences 1
## 365 2 2 Medical 1
## 491 10 4 Life Sciences 1
## 616 5 2 Medical 1
## 748 1 3 Medical 1
## 123 9 3 Marketing 1
## 841 3 3 Life Sciences 1
## 326 4 3 Life Sciences 1
## 149 24 3 Medical 1
## 91 2 2 Life Sciences 1
## 72 5 3 Medical 1
## 563 16 4 Technical Degree 1
## 80 2 1 Medical 1
## 828 2 3 Marketing 1
## 481 1 3 Life Sciences 1
## 466 2 3 Life Sciences 1
## 510 12 3 Technical Degree 1
## 381 5 5 Life Sciences 1
## 90 2 4 Marketing 1
## 127 16 1 Life Sciences 1
## 737 24 1 Marketing 1
## 527 4 2 Life Sciences 1
## 100 1 3 Life Sciences 1
## 209 5 3 Technical Degree 1
## 148 8 4 Life Sciences 1
## 125 2 2 Life Sciences 1
## 754 10 3 Life Sciences 1
## 139 6 3 Medical 1
## 649 1 4 Marketing 1
## 516 2 3 Medical 1
## 3 18 2 Life Sciences 1
## 412 6 3 Marketing 1
## 667 1 1 Medical 1
## 227 4 2 Other 1
## 4 1 4 Marketing 1
## 27 14 3 Life Sciences 1
## 240 17 5 Life Sciences 1
## 169 6 2 Technical Degree 1
## 422 25 5 Medical 1
## 130 3 3 Technical Degree 1
## 784 25 3 Medical 1
## 188 2 4 Life Sciences 1
## 521 16 4 Life Sciences 1
## 745 8 2 Other 1
## 256 9 3 Life Sciences 1
## 145 2 1 Medical 1
## 262 1 4 Technical Degree 1
## 369 10 3 Technical Degree 1
## 573 1 2 Life Sciences 1
## 135 10 4 Life Sciences 1
## 152 10 3 Marketing 1
## 374 10 3 Medical 1
## 822 20 2 Marketing 1
## 785 2 4 Marketing 1
## 446 1 3 Technical Degree 1
## 793 2 2 Medical 1
## 17 3 4 Medical 1
## 718 10 3 Other 1
## 176 1 1 Technical Degree 1
## 319 21 4 Life Sciences 1
## 372 22 4 Other 1
## 461 5 2 Other 1
## 361 1 4 Life Sciences 1
## 141 23 3 Life Sciences 1
## 28 9 4 Marketing 1
## 423 6 4 Life Sciences 1
## 363 2 1 Marketing 1
## 796 3 3 Life Sciences 1
## 859 9 4 Life Sciences 1
## 295 6 2 Medical 1
## 574 8 2 Technical Degree 1
## 703 8 2 Marketing 1
## 334 1 2 Life Sciences 1
## 268 2 4 Life Sciences 1
## 272 3 3 Life Sciences 1
## 671 7 4 Medical 1
## 51 1 1 Life Sciences 1
## 153 7 3 Life Sciences 1
## 441 2 2 Life Sciences 1
## 154 5 3 Life Sciences 1
## 87 14 4 Marketing 1
## 575 28 2 Marketing 1
## 414 10 4 Marketing 1
## 763 10 2 Medical 1
## 58 3 1 Medical 1
## 224 1 3 Life Sciences 1
## 580 7 3 Life Sciences 1
## 622 16 4 Medical 1
## 155 10 3 Life Sciences 1
## 847 3 1 Medical 1
## 542 8 4 Life Sciences 1
## 395 2 1 Medical 1
## 67 6 4 Life Sciences 1
## 25 1 3 Life Sciences 1
## 288 19 2 Medical 1
## 358 25 5 Technical Degree 1
## 827 16 4 Marketing 1
## 728 10 4 Medical 1
## 730 2 1 Technical Degree 1
## 303 22 3 Technical Degree 1
## 69 1 3 Medical 1
## 264 20 3 Technical Degree 1
## 562 15 4 Life Sciences 1
## 675 8 3 Medical 1
## 427 7 3 Technical Degree 1
## 223 16 3 Life Sciences 1
## 35 5 4 Life Sciences 1
## 533 23 2 Life Sciences 1
## 239 2 3 Human Resources 1
## 243 2 3 Life Sciences 1
## 707 7 4 Medical 1
## 43 16 3 Medical 1
## 777 1 3 Life Sciences 1
## 419 1 3 Life Sciences 1
## 333 1 2 Medical 1
## 640 2 1 Marketing 1
## 757 21 1 Life Sciences 1
## 774 9 3 Medical 1
## 864 3 3 Medical 1
## 499 27 3 Medical 1
## 484 8 3 Life Sciences 1
## 281 2 3 Medical 1
## 84 6 3 Medical 1
## 761 10 3 Life Sciences 1
## 37 10 3 Medical 1
## 292 24 4 Life Sciences 1
## 537 2 2 Marketing 1
## 118 13 2 Other 1
## 261 2 3 Life Sciences 1
## 213 10 2 Life Sciences 1
## 459 1 2 Life Sciences 1
## 593 1 3 Technical Degree 1
## 448 13 3 Medical 1
## 26 5 3 Marketing 1
## 599 4 3 Life Sciences 1
## 113 7 4 Life Sciences 1
## 345 29 4 Life Sciences 1
## 870 2 3 Medical 1
## 177 2 3 Medical 1
## 156 25 2 Technical Degree 1
## 568 1 3 Life Sciences 1
## 673 7 3 Life Sciences 1
## 432 16 2 Life Sciences 1
## 759 4 2 Medical 1
## 440 1 4 Life Sciences 1
## 347 2 2 Life Sciences 1
## 833 19 4 Medical 1
## 274 16 4 Marketing 1
## 40 20 3 Life Sciences 1
## 636 4 4 Other 1
## 2 14 3 Medical 1
## 216 2 3 Medical 1
## 312 6 3 Life Sciences 1
## 305 2 3 Technical Degree 1
## 838 21 4 Life Sciences 1
## 686 9 1 Life Sciences 1
## 162 24 2 Technical Degree 1
## 866 4 3 Life Sciences 1
## 832 22 1 Marketing 1
## 627 4 1 Medical 1
## 329 8 3 Medical 1
## 179 8 3 Marketing 1
## 545 2 4 Medical 1
## 397 2 3 Life Sciences 1
## 185 8 1 Life Sciences 1
## 357 6 3 Other 1
## 170 28 4 Life Sciences 1
## 251 1 2 Medical 1
## 638 13 4 Medical 1
## 869 7 3 Medical 1
## 429 1 3 Life Sciences 1
## 57 19 3 Medical 1
## 705 6 2 Medical 1
## 55 8 4 Life Sciences 1
## 853 15 2 Life Sciences 1
## 478 29 2 Medical 1
## 797 20 2 Medical 1
## 770 9 4 Life Sciences 1
## 257 5 3 Marketing 1
## 79 6 3 Medical 1
## 66 5 2 Life Sciences 1
## 687 14 1 Other 1
## 339 1 4 Other 1
## 631 2 4 Technical Degree 1
## 676 4 2 Marketing 1
## 143 7 2 Medical 1
## 561 3 2 Medical 1
## 254 18 5 Human Resources 1
## 189 4 1 Life Sciences 1
## 1 13 4 Life Sciences 1
## 858 7 2 Life Sciences 1
## 578 5 3 Life Sciences 1
## 234 3 3 Marketing 1
## 751 9 3 Marketing 1
## 299 9 2 Medical 1
## 543 3 3 Medical 1
## 214 1 2 Life Sciences 1
## 366 6 3 Life Sciences 1
## 624 26 4 Life Sciences 1
## 610 8 1 Life Sciences 1
## 250 2 5 Life Sciences 1
## 119 14 3 Life Sciences 1
## 103 4 4 Marketing 1
## 221 2 3 Other 1
## 630 9 3 Medical 1
## 605 9 3 Marketing 1
## 462 7 3 Life Sciences 1
## 714 3 4 Medical 1
## 517 19 2 Marketing 1
## 287 7 1 Life Sciences 1
## 519 6 3 Technical Degree 1
## 674 25 4 Life Sciences 1
## 392 23 3 Marketing 1
## 758 5 4 Technical Degree 1
## 514 1 2 Life Sciences 1
## 668 10 1 Medical 1
## 317 2 4 Marketing 1
## 52 1 3 Medical 1
## 511 10 2 Life Sciences 1
## 815 9 3 Marketing 1
## 341 5 4 Life Sciences 1
## 612 2 4 Other 1
## 564 10 3 Medical 1
## 183 28 3 Medical 1
## 97 3 3 Life Sciences 1
## 602 1 4 Technical Degree 1
## 711 17 4 Medical 1
## 586 19 2 Medical 1
## 642 12 3 Life Sciences 1
## 651 1 3 Medical 1
## 666 1 3 Life Sciences 1
## 508 2 3 Marketing 1
## 70 1 3 Life Sciences 1
## 73 23 2 Life Sciences 1
## 509 4 4 Marketing 1
## 704 20 2 Medical 1
## 455 1 4 Life Sciences 1
## 662 2 4 Life Sciences 1
## 7 5 5 Medical 1
## 198 8 4 Technical Degree 1
## 442 9 4 Medical 1
## 756 24 3 Medical 1
## 322 1 5 Life Sciences 1
## 265 18 1 Life Sciences 1
## 150 27 3 Medical 1
## 232 2 3 Marketing 1
## 421 1 2 Medical 1
## 231 2 2 Other 1
## 786 8 5 Life Sciences 1
## 241 24 3 Medical 1
## 228 5 3 Life Sciences 1
## 284 7 3 Marketing 1
## 99 2 1 Life Sciences 1
## 181 2 3 Life Sciences 1
## 496 26 4 Marketing 1
## 39 8 4 Life Sciences 1
## 212 5 2 Marketing 1
## 107 1 1 Technical Degree 1
## 325 1 1 Life Sciences 1
## 635 7 3 Life Sciences 1
## 343 5 3 Life Sciences 1
## 237 2 3 Life Sciences 1
## 206 9 2 Life Sciences 1
## 607 1 1 Life Sciences 1
## 318 1 2 Life Sciences 1
## 772 1 2 Marketing 1
## 645 5 2 Life Sciences 1
## 749 29 1 Medical 1
## 385 4 4 Life Sciences 1
## 794 8 3 Technical Degree 1
## 313 3 3 Life Sciences 1
## 380 22 3 Life Sciences 1
## 260 2 1 Other 1
## 10 10 4 Technical Degree 1
## 447 1 3 Life Sciences 1
## 534 15 4 Medical 1
## 497 10 2 Life Sciences 1
## 352 7 3 Technical Degree 1
## 746 21 2 Technical Degree 1
## 158 2 3 Life Sciences 1
## 483 22 4 Other 1
## 323 5 4 Life Sciences 1
## 324 8 4 Life Sciences 1
## 535 8 4 Other 1
## 307 9 3 Life Sciences 1
## 694 4 3 Technical Degree 1
## 750 2 3 Life Sciences 1
## 16 20 2 Marketing 1
## 855 16 2 Other 1
## 823 28 3 Marketing 1
## 591 26 2 Other 1
## 360 6 3 Life Sciences 1
## 470 13 2 Medical 1
## 68 12 2 Other 1
## 548 16 4 Medical 1
## 670 3 3 Life Sciences 1
## 424 1 3 Life Sciences 1
## 121 8 3 Life Sciences 1
## 818 7 3 Medical 1
## 258 7 4 Marketing 1
## 696 2 4 Life Sciences 1
## 812 1 3 Technical Degree 1
## 479 8 4 Other 1
## 490 8 2 Medical 1
## 867 26 4 Marketing 1
## 471 2 4 Technical Degree 1
## 862 4 4 Marketing 1
## 824 15 3 Medical 1
## 522 24 3 Medical 1
## 401 2 3 Life Sciences 1
## 353 26 2 Medical 1
## 163 8 4 Marketing 1
## 492 28 3 Life Sciences 1
## 820 13 2 Marketing 1
## 632 1 3 Marketing 1
## 384 2 1 Technical Degree 1
## 644 16 4 Technical Degree 1
## 792 27 2 Medical 1
## 525 3 3 Life Sciences 1
## 677 1 3 Other 1
## 807 18 1 Life Sciences 1
## 825 12 4 Life Sciences 1
## 551 1 3 Human Resources 1
## 328 18 4 Life Sciences 1
## 356 3 3 Other 1
## 77 1 3 Technical Degree 1
## 116 19 2 Medical 1
## 450 26 3 Marketing 1
## 701 6 1 Medical 1
## 443 2 4 Life Sciences 1
## 739 18 3 Medical 1
## 835 8 1 Medical 1
## 160 26 5 Medical 1
## 850 6 3 Other 1
## 604 6 1 Medical 1
## 165 16 3 Marketing 1
## 606 7 3 Life Sciences 1
## 524 18 4 Life Sciences 1
## 126 7 4 Life Sciences 1
## 93 2 2 Other 1
## 418 18 4 Life Sciences 1
## 64 14 3 Medical 1
## 685 21 3 Medical 1
## 293 9 3 Medical 1
## 727 21 2 Medical 1
## 729 24 3 Technical Degree 1
## 860 15 3 Medical 1
## 609 7 3 Other 1
## 246 7 1 Medical 1
## 375 9 1 Life Sciences 1
## 159 27 3 Life Sciences 1
## 108 26 4 Human Resources 1
## 489 1 2 Life Sciences 1
## 391 8 2 Life Sciences 1
## 82 27 4 Life Sciences 1
## 379 15 1 Life Sciences 1
## 439 3 2 Marketing 1
## 681 4 1 Marketing 1
## 477 16 4 Marketing 1
## 314 7 4 Medical 1
## 843 25 4 Medical 1
## 863 19 3 Marketing 1
## 839 4 3 Life Sciences 1
## 362 24 3 Technical Degree 1
## 660 11 3 Life Sciences 1
## 643 2 1 Medical 1
## 310 8 3 Life Sciences 1
## 45 1 2 Life Sciences 1
## 390 1 1 Technical Degree 1
## 193 18 3 Medical 1
## 494 16 3 Life Sciences 1
## 34 1 4 Medical 1
## 78 1 4 Life Sciences 1
## 344 3 3 Medical 1
## 753 20 5 Medical 1
## 202 1 3 Life Sciences 1
## 6 10 2 Life Sciences 1
## 36 7 3 Life Sciences 1
## 849 22 2 Marketing 1
## 340 2 4 Life Sciences 1
## 132 11 4 Marketing 1
## 373 7 3 Life Sciences 1
## 747 13 3 Human Resources 1
## 44 9 2 Other 1
## 267 15 3 Medical 1
## 410 14 2 Life Sciences 1
## 760 2 3 Marketing 1
## 788 8 4 Medical 1
## 596 1 3 Medical 1
## 302 18 5 Life Sciences 1
## 731 8 3 Life Sciences 1
## 56 9 4 Life Sciences 1
## 454 10 4 Marketing 1
## 382 25 1 Life Sciences 1
## 647 8 1 Medical 1
## 445 2 4 Medical 1
## 804 4 3 Technical Degree 1
## 104 4 3 Medical 1
## 437 2 4 Medical 1
## 656 11 2 Medical 1
## 679 4 2 Life Sciences 1
## 752 1 3 Life Sciences 1
## 426 25 3 Medical 1
## 296 11 4 Medical 1
## 420 3 4 Medical 1
## 830 3 3 Medical 1
## 32 21 3 Medical 1
## 726 1 3 Life Sciences 1
## 166 2 3 Medical 1
## 247 7 4 Medical 1
## 117 10 4 Life Sciences 1
## 845 3 2 Life Sciences 1
## 364 2 2 Medical 1
## 557 8 4 Life Sciences 1
## 376 22 5 Medical 1
## 98 2 2 Life Sciences 1
## 502 24 3 Life Sciences 1
## 736 2 2 Medical 1
## 180 1 4 Life Sciences 1
## 338 3 1 Life Sciences 1
## 501 7 4 Medical 1
## 778 2 1 Life Sciences 1
## 531 17 1 Life Sciences 1
## 744 4 1 Medical 1
## 196 1 3 Life Sciences 1
## 30 19 4 Medical 1
## 31 9 2 Medical 1
## 400 2 1 Medical 1
## 95 11 3 Life Sciences 1
## 49 5 3 Marketing 1
## 771 10 3 Marketing 1
## 659 6 3 Medical 1
## 465 17 3 Technical Degree 1
## 698 5 2 Life Sciences 1
## 798 8 1 Medical 1
## 782 2 4 Technical Degree 1
## 291 1 4 Medical 1
## 764 1 3 Life Sciences 1
## 211 28 4 Life Sciences 1
## 634 3 2 Life Sciences 1
## 654 29 1 Medical 1
## 775 2 3 Life Sciences 1
## 559 29 3 Life Sciences 1
## 805 23 3 Medical 1
## 178 2 3 Life Sciences 1
## 110 2 3 Technical Degree 1
## 857 3 3 Technical Degree 1
## 337 11 2 Marketing 1
## 115 17 4 Life Sciences 1
## 585 10 4 Other 1
## 460 4 4 Life Sciences 1
## 505 2 4 Medical 1
## 576 1 4 Life Sciences 1
## 799 10 3 Medical 1
## 856 7 1 Life Sciences 1
## 233 9 3 Life Sciences 1
## 780 1 4 Medical 1
## 129 6 3 Medical 1
## 837 4 1 Life Sciences 1
## 766 5 5 Medical 1
## 569 14 2 Medical 1
## 388 19 3 Technical Degree 1
## 321 1 3 Life Sciences 1
## 308 3 4 Life Sciences 1
## 829 2 2 Medical 1
## 94 10 4 Medical 1
## 289 11 2 Technical Degree 1
## 408 6 4 Life Sciences 1
## 368 9 2 Medical 1
## 164 17 2 Life Sciences 1
## 413 19 2 Life Sciences 1
## 487 9 3 Medical 1
## 767 7 2 Technical Degree 1
## 733 22 3 Human Resources 1
## 735 7 4 Medical 1
## 808 19 1 Medical 1
## 458 21 4 Marketing 1
## 552 8 2 Medical 1
## 53 2 1 Life Sciences 1
## 409 2 4 Life Sciences 1
## 702 9 1 Life Sciences 1
## 280 4 4 Life Sciences 1
## 327 7 3 Medical 1
## 415 6 2 Marketing 1
## 83 6 4 Life Sciences 1
## 396 2 4 Life Sciences 1
## 133 28 4 Technical Degree 1
## 275 14 4 Medical 1
## 456 7 4 Medical 1
## 518 12 3 Medical 1
## 304 9 4 Medical 1
## 238 9 4 Life Sciences 1
## 507 5 3 Marketing 1
## 482 11 4 Life Sciences 1
## 476 5 4 Marketing 1
## 434 4 3 Technical Degree 1
## 495 10 4 Technical Degree 1
## 724 12 5 Medical 1
## 47 1 2 Life Sciences 1
## 147 14 3 Medical 1
## 331 1 3 Life Sciences 1
## 218 22 3 Medical 1
## 555 9 5 Life Sciences 1
## 201 24 3 Life Sciences 1
## 587 6 3 Medical 1
## 290 8 4 Life Sciences 1
## 467 2 4 Life Sciences 1
## 393 3 2 Medical 1
## 255 8 1 Medical 1
## 558 1 2 Life Sciences 1
## 300 2 4 Medical 1
## 253 2 3 Life Sciences 1
## 592 24 4 Medical 1
## 59 1 4 Medical 1
## 811 9 3 Medical 1
## 278 10 4 Life Sciences 1
## 137 1 2 Life Sciences 1
## 225 9 2 Life Sciences 1
## 191 28 2 Medical 1
## 544 2 4 Life Sciences 1
## 473 6 4 Other 1
## 523 7 3 Life Sciences 1
## 710 2 4 Life Sciences 1
## 861 6 2 Medical 1
## 182 2 2 Life Sciences 1
## 852 9 2 Medical 1
## 236 1 4 Life Sciences 1
## 779 7 3 Medical 1
## 187 2 1 Life Sciences 1
## 717 10 3 Marketing 1
## 740 25 2 Other 1
## 629 2 1 Life Sciences 1
## 311 1 4 Life Sciences 1
## 399 1 4 Life Sciences 1
## 131 5 3 Medical 1
## 486 1 2 Life Sciences 1
## 370 2 4 Medical 1
## 199 7 4 Medical 1
## 783 3 2 Human Resources 1
## 566 1 3 Medical 1
## 226 1 2 Medical 1
## 488 26 1 Life Sciences 1
## 614 21 3 Other 1
## 320 25 3 Life Sciences 1
## 800 3 2 Life Sciences 1
## 700 15 2 Life Sciences 1
## 762 2 2 Other 1
## 684 24 2 Life Sciences 1
## 220 4 2 Life Sciences 1
## 229 3 2 Other 1
## 11 6 3 Medical 1
## 342 2 3 Life Sciences 1
## 716 22 1 Human Resources 1
## 65 17 2 Life Sciences 1
## 259 4 3 Life Sciences 1
## 540 4 4 Marketing 1
## 24 2 4 Other 1
## 134 12 3 Medical 1
## 590 23 4 Medical 1
## 286 28 4 Life Sciences 1
## 12 1 2 Life Sciences 1
## 249 22 3 Life Sciences 1
## 529 1 3 Medical 1
## 215 3 4 Medical 1
## 449 29 5 Life Sciences 1
## 20 1 4 Life Sciences 1
## 530 14 4 Life Sciences 1
## 294 1 1 Medical 1
## 89 8 3 Life Sciences 1
## 435 10 4 Marketing 1
## 436 8 4 Marketing 1
## 330 10 3 Human Resources 1
## 639 23 3 Medical 1
## 663 2 3 Life Sciences 1
## 469 29 2 Medical 1
## 868 14 3 Medical 1
## 713 2 2 Technical Degree 1
## 594 25 4 Life Sciences 1
## 579 4 3 Medical 1
## 646 4 4 Marketing 1
## 633 3 4 Life Sciences 1
## 581 19 1 Medical 1
## 513 10 3 Life Sciences 1
## 619 6 1 Medical 1
## 282 6 3 Medical 1
## 791 3 2 Medical 1
## 699 3 2 Medical 1
## 601 13 2 Life Sciences 1
## 210 9 1 Life Sciences 1
## 172 14 4 Technical Degree 1
## 22 2 1 Marketing 1
## 453 27 3 Life Sciences 1
## 776 10 1 Medical 1
## 626 4 2 Life Sciences 1
## 252 8 4 Technical Degree 1
## 480 10 3 Life Sciences 1
## 648 17 4 Medical 1
## 840 10 3 Life Sciences 1
## 814 26 4 Life Sciences 1
## 285 1 3 Human Resources 1
## 438 1 3 Medical 1
## 279 5 3 Marketing 1
## 203 4 3 Life Sciences 1
## 405 27 1 Medical 1
## 138 17 3 Technical Degree 1
## 217 10 3 Medical 1
## 854 16 2 Medical 1
## EmployeeNumber EnvironmentSatisfaction Gender HourlyRate
## 834 1193 3 Male 54
## 697 1644 3 Male 89
## 207 1188 1 Female 85
## 715 789 1 Male 76
## 809 1729 2 Male 33
## 725 1860 4 Male 81
## 222 684 1 Female 100
## 71 1999 4 Male 76
## 503 1691 4 Female 95
## 403 1856 2 Female 64
## 387 314 3 Female 92
## 806 1790 4 Male 90
## 826 1069 4 Male 40
## 821 1607 2 Female 35
## 844 1344 4 Male 86
## 528 1251 3 Male 80
## 128 1888 2 Female 93
## 539 554 1 Female 62
## 457 1673 1 Female 67
## 732 445 4 Female 41
## 842 1419 2 Male 51
## 85 1294 3 Female 53
## 122 2021 3 Male 58
## 474 192 3 Male 83
## 819 198 2 Male 35
## 625 1504 3 Male 38
## 184 1525 2 Female 32
## 92 1165 4 Female 45
## 269 1970 4 Male 36
## 463 174 3 Female 77
## 407 1784 1 Female 91
## 235 158 3 Male 92
## 54 972 3 Male 57
## 146 469 4 Male 61
## 464 1736 4 Female 30
## 192 1833 1 Female 68
## 665 60 2 Male 72
## 394 573 3 Female 80
## 810 233 3 Male 51
## 512 1397 3 Male 71
## 628 461 3 Male 92
## 248 2013 2 Male 68
## 8 1105 4 Female 88
## 468 97 2 Female 55
## 851 175 2 Female 71
## 404 1651 2 Female 42
## 365 1336 4 Female 58
## 491 396 3 Female 35
## 616 1696 2 Male 57
## 748 1184 4 Male 97
## 123 864 4 Male 74
## 841 405 3 Male 54
## 326 933 2 Female 76
## 149 1173 4 Male 49
## 91 556 4 Male 42
## 72 1469 4 Male 84
## 563 1001 3 Female 37
## 80 1974 4 Female 59
## 828 1281 3 Female 86
## 481 1954 1 Male 89
## 466 24 4 Male 45
## 510 58 2 Female 49
## 381 447 4 Male 42
## 90 38 2 Female 83
## 127 33 2 Female 72
## 737 2056 2 Female 60
## 527 240 3 Female 30
## 100 1447 4 Female 86
## 209 197 4 Female 84
## 148 1560 1 Male 46
## 125 388 2 Male 44
## 754 1869 3 Female 64
## 139 932 4 Male 70
## 649 100 3 Male 32
## 516 408 4 Male 84
## 3 1412 3 Male 60
## 412 1708 4 Male 35
## 667 312 2 Female 42
## 227 53 3 Male 75
## 4 2016 3 Female 48
## 27 1582 3 Male 80
## 240 1656 4 Female 50
## 169 630 4 Male 78
## 422 529 2 Female 72
## 130 1863 1 Female 45
## 784 1220 2 Female 82
## 188 1676 2 Female 47
## 521 838 3 Female 100
## 745 2018 1 Female 85
## 256 1813 1 Male 52
## 145 1375 2 Female 89
## 262 1522 1 Male 100
## 369 1592 4 Male 45
## 573 1927 3 Male 42
## 135 448 3 Female 82
## 152 722 4 Male 55
## 374 1640 2 Female 71
## 822 349 4 Male 45
## 785 1731 3 Male 87
## 446 1438 1 Male 74
## 793 1638 4 Female 54
## 17 966 3 Female 93
## 718 1097 3 Female 59
## 176 1131 4 Female 70
## 319 1390 1 Male 44
## 372 567 3 Female 69
## 461 591 2 Male 97
## 361 259 3 Male 65
## 141 1373 4 Male 68
## 28 1157 1 Female 77
## 423 163 2 Male 76
## 363 1038 1 Female 57
## 796 702 1 Male 70
## 859 200 3 Male 94
## 295 1550 3 Male 38
## 574 1823 2 Male 92
## 703 1243 3 Female 78
## 334 725 3 Female 68
## 268 1692 4 Male 95
## 272 1658 4 Female 66
## 671 102 1 Male 30
## 51 662 3 Female 43
## 153 420 4 Male 91
## 441 1235 4 Female 38
## 154 1814 2 Male 85
## 87 728 4 Male 42
## 575 641 1 Male 66
## 414 1853 4 Male 79
## 763 1329 3 Male 40
## 58 1586 2 Male 87
## 224 1506 1 Male 72
## 580 1260 3 Male 82
## 622 987 3 Male 64
## 155 934 2 Male 83
## 847 201 2 Female 79
## 542 1798 4 Male 90
## 395 875 2 Male 35
## 67 691 2 Male 82
## 25 969 2 Female 42
## 288 18 2 Male 93
## 358 565 3 Female 71
## 827 1761 1 Male 70
## 728 277 4 Male 43
## 730 169 3 Male 100
## 303 1694 1 Male 94
## 69 88 2 Male 79
## 264 974 3 Female 84
## 562 1837 1 Male 87
## 675 1647 2 Female 86
## 427 1482 3 Male 64
## 223 1681 3 Female 36
## 35 1966 4 Male 56
## 533 824 3 Male 81
## 239 1987 3 Female 82
## 243 94 3 Male 76
## 707 1830 4 Female 68
## 43 1674 4 Female 74
## 777 1499 3 Male 46
## 419 1196 4 Female 56
## 333 1792 3 Male 99
## 640 667 3 Male 85
## 757 942 4 Female 51
## 774 90 4 Male 79
## 864 746 4 Female 49
## 499 13 3 Male 94
## 484 241 3 Female 67
## 281 1706 4 Male 73
## 84 474 3 Male 51
## 761 994 4 Female 92
## 37 1639 4 Male 55
## 292 419 2 Female 47
## 537 1125 4 Male 32
## 118 2053 4 Male 46
## 261 1379 3 Female 90
## 213 715 4 Male 80
## 459 1441 1 Female 90
## 593 1486 1 Male 92
## 448 1803 2 Male 95
## 26 1267 2 Male 88
## 599 2064 2 Male 87
## 113 1118 2 Male 87
## 345 1211 3 Male 61
## 870 269 2 Female 37
## 177 787 4 Male 78
## 156 475 4 Female 85
## 568 1562 2 Male 64
## 673 1507 3 Male 55
## 432 1340 3 Male 74
## 759 1687 4 Female 87
## 440 724 2 Female 33
## 347 1140 4 Male 95
## 833 1216 4 Male 78
## 274 1436 3 Female 98
## 40 1135 3 Male 67
## 636 1185 4 Male 47
## 2 1128 3 Male 44
## 216 1474 3 Male 68
## 312 601 2 Female 75
## 305 977 4 Male 51
## 838 621 4 Male 74
## 686 581 3 Male 33
## 162 1082 2 Male 72
## 866 1363 4 Male 54
## 832 167 4 Male 50
## 627 1445 4 Female 42
## 329 1937 4 Female 58
## 179 207 2 Female 43
## 545 804 3 Female 86
## 397 575 1 Male 51
## 185 602 3 Female 48
## 357 164 3 Female 46
## 170 1120 3 Female 93
## 251 1994 2 Male 59
## 638 1951 4 Male 39
## 869 1195 1 Female 83
## 429 331 4 Male 62
## 57 752 3 Male 57
## 705 321 2 Male 52
## 55 98 3 Female 61
## 853 15 4 Female 49
## 478 618 1 Male 45
## 797 372 3 Male 79
## 770 1117 2 Female 65
## 257 399 4 Female 84
## 79 302 4 Female 57
## 66 21 1 Male 80
## 687 1421 3 Female 84
## 339 1113 4 Male 81
## 631 1873 2 Male 98
## 676 1280 4 Female 33
## 143 1007 4 Male 75
## 561 238 4 Male 30
## 254 1842 4 Male 89
## 189 385 1 Male 96
## 1 859 2 Male 73
## 858 442 3 Female 48
## 578 1471 3 Male 90
## 234 303 4 Female 52
## 751 1077 4 Female 54
## 299 991 3 Male 46
## 543 224 3 Male 79
## 214 827 4 Female 43
## 366 532 3 Female 86
## 624 2040 3 Female 31
## 610 1044 2 Female 88
## 250 625 2 Female 91
## 119 227 1 Male 56
## 103 523 3 Female 72
## 221 786 4 Male 60
## 630 717 4 Female 46
## 605 1752 2 Female 52
## 462 1659 1 Male 77
## 714 481 1 Male 48
## 517 952 3 Male 36
## 287 1878 4 Male 75
## 519 1311 1 Female 48
## 674 881 4 Female 96
## 392 72 3 Female 47
## 758 1473 1 Male 96
## 514 73 1 Female 98
## 668 584 1 Male 38
## 317 216 3 Female 75
## 52 1423 4 Female 76
## 511 2041 1 Female 100
## 815 893 2 Male 98
## 341 51 2 Female 82
## 612 616 1 Female 62
## 564 1979 3 Female 40
## 183 171 2 Female 72
## 97 1527 3 Female 51
## 602 1574 4 Male 62
## 711 1321 2 Female 95
## 586 281 3 Male 99
## 642 1829 2 Female 47
## 651 1564 4 Male 68
## 666 1753 4 Male 70
## 508 1391 2 Male 52
## 70 391 4 Female 42
## 73 261 2 Male 50
## 509 311 1 Male 41
## 704 1818 4 Female 69
## 455 664 4 Female 40
## 662 579 4 Male 62
## 7 1448 2 Male 90
## 198 1138 4 Male 56
## 442 1709 4 Female 43
## 756 744 1 Male 49
## 322 683 2 Male 42
## 265 150 2 Male 52
## 150 1622 2 Female 39
## 232 49 4 Female 97
## 421 110 1 Male 94
## 231 1635 4 Female 59
## 786 1430 1 Female 93
## 241 605 4 Male 56
## 228 284 4 Male 60
## 284 438 4 Male 73
## 99 1513 4 Female 97
## 181 1182 3 Male 44
## 496 851 1 Female 66
## 39 1338 4 Male 99
## 212 1029 4 Male 90
## 107 208 4 Male 41
## 325 1026 4 Female 65
## 635 451 3 Female 43
## 343 1099 4 Male 34
## 237 2031 1 Male 56
## 206 913 2 Male 61
## 607 91 1 Female 57
## 318 1981 2 Male 45
## 772 244 2 Female 92
## 645 1210 1 Male 95
## 749 1725 2 Male 91
## 385 1081 1 Male 34
## 794 83 3 Female 59
## 313 1537 4 Female 33
## 380 1004 1 Female 48
## 260 1992 4 Male 57
## 10 1597 4 Male 92
## 447 1668 4 Male 90
## 534 1955 3 Female 34
## 497 544 1 Male 69
## 352 416 2 Male 100
## 746 1551 3 Male 57
## 158 1127 1 Male 84
## 483 1932 3 Male 86
## 323 861 2 Male 75
## 324 1080 4 Female 74
## 535 454 2 Male 77
## 307 1030 1 Male 64
## 694 514 3 Male 40
## 750 242 3 Male 90
## 16 947 4 Female 45
## 855 1886 4 Female 44
## 823 2035 4 Female 95
## 591 1605 2 Female 85
## 360 373 4 Male 47
## 470 250 4 Female 57
## 68 1982 4 Male 78
## 548 832 1 Male 51
## 670 1250 4 Male 85
## 424 1368 4 Female 97
## 121 742 1 Female 63
## 818 817 3 Male 49
## 258 1428 4 Female 73
## 696 925 4 Female 65
## 812 1415 1 Male 81
## 479 1409 3 Female 54
## 490 757 2 Female 96
## 867 333 3 Male 100
## 471 1931 2 Male 41
## 862 1293 4 Female 56
## 824 1160 3 Male 72
## 522 1604 3 Male 51
## 401 548 3 Female 43
## 353 1180 3 Female 92
## 163 677 3 Male 39
## 492 1317 4 Female 43
## 820 1933 4 Female 84
## 632 1950 1 Male 32
## 384 113 3 Male 62
## 644 1905 4 Male 85
## 792 247 4 Female 95
## 525 309 3 Male 70
## 677 823 3 Female 35
## 807 1399 1 Male 93
## 825 1614 3 Female 76
## 551 734 4 Male 59
## 328 1998 4 Male 35
## 356 1478 4 Male 81
## 77 1535 4 Male 64
## 116 904 1 Male 32
## 450 1612 1 Male 52
## 701 1865 4 Male 87
## 443 364 1 Male 79
## 739 975 3 Female 86
## 835 1278 4 Male 37
## 160 1242 1 Female 60
## 850 1918 3 Male 61
## 604 1192 2 Female 54
## 165 406 3 Male 96
## 606 730 3 Male 59
## 524 638 4 Male 58
## 126 1962 3 Female 55
## 93 4 4 Male 92
## 418 407 3 Male 81
## 64 1839 2 Female 33
## 685 221 3 Male 42
## 293 260 4 Female 99
## 727 900 3 Female 54
## 729 622 3 Male 66
## 860 1295 1 Female 73
## 609 1417 3 Male 100
## 246 912 1 Female 62
## 375 1680 4 Male 54
## 159 1126 3 Female 83
## 108 1973 3 Male 71
## 489 64 1 Male 98
## 391 808 3 Female 93
## 82 1728 4 Male 49
## 379 1202 2 Female 69
## 439 47 1 Male 86
## 681 885 3 Male 87
## 477 868 4 Male 66
## 314 211 2 Male 34
## 843 867 3 Female 81
## 863 502 1 Male 67
## 839 1827 3 Female 92
## 362 1928 3 Male 45
## 660 85 2 Male 95
## 643 1074 3 Male 40
## 310 707 4 Male 57
## 45 1270 4 Male 47
## 390 478 1 Female 99
## 193 550 3 Female 87
## 494 1529 4 Female 91
## 34 1172 4 Male 58
## 78 1602 4 Male 56
## 344 680 3 Female 71
## 753 1606 2 Male 41
## 202 96 2 Male 66
## 6 733 4 Male 32
## 36 359 2 Male 100
## 849 492 3 Male 68
## 340 54 4 Female 33
## 132 1757 4 Female 80
## 373 425 1 Male 97
## 747 1844 1 Male 56
## 44 1595 4 Male 93
## 267 1485 2 Male 71
## 410 843 2 Male 94
## 760 1277 1 Female 94
## 788 433 3 Male 85
## 596 855 1 Male 48
## 302 74 2 Male 71
## 731 920 1 Female 55
## 56 1772 3 Male 94
## 454 1924 2 Male 43
## 382 177 3 Female 61
## 647 1156 3 Male 80
## 445 1789 3 Male 46
## 804 1100 4 Male 86
## 104 705 1 Male 58
## 437 828 3 Male 46
## 656 390 1 Male 60
## 679 2025 4 Female 97
## 752 1331 4 Female 54
## 426 1076 3 Male 79
## 296 2003 4 Male 43
## 420 564 2 Male 93
## 830 644 1 Male 99
## 32 1698 2 Male 79
## 726 350 1 Female 62
## 166 922 3 Female 49
## 247 462 3 Female 39
## 117 1240 3 Male 57
## 845 1779 4 Female 32
## 364 352 1 Male 49
## 557 578 3 Female 42
## 376 1539 3 Male 88
## 98 139 3 Male 100
## 502 19 3 Male 50
## 736 1898 1 Female 89
## 180 230 1 Female 59
## 338 1103 1 Male 45
## 501 1360 3 Female 53
## 778 1190 3 Female 76
## 531 1088 3 Female 65
## 744 1735 3 Female 51
## 196 353 3 Male 96
## 30 791 4 Male 41
## 31 195 4 Male 35
## 400 1496 2 Male 86
## 95 226 3 Female 53
## 49 614 2 Male 69
## 771 446 2 Male 46
## 659 466 4 Male 95
## 465 1767 3 Male 38
## 698 304 4 Male 62
## 798 1783 3 Female 79
## 782 721 4 Female 80
## 291 996 4 Female 91
## 764 1040 4 Female 95
## 211 2034 1 Female 60
## 634 155 1 Female 39
## 654 473 1 Female 91
## 775 1547 4 Male 40
## 559 836 2 Male 98
## 805 132 2 Male 67
## 178 1053 1 Male 57
## 110 2038 4 Male 78
## 857 297 4 Female 88
## 337 1137 4 Male 54
## 115 1218 3 Male 94
## 585 40 4 Male 41
## 460 1435 1 Male 37
## 505 1443 1 Female 82
## 576 77 1 Male 51
## 799 1248 1 Female 96
## 856 1492 4 Male 76
## 233 1716 3 Male 82
## 780 2027 1 Male 36
## 129 1402 1 Female 73
## 837 1394 3 Male 54
## 766 1611 3 Female 50
## 569 936 3 Female 68
## 388 611 4 Male 34
## 321 951 3 Female 70
## 308 1115 3 Male 40
## 829 1760 3 Female 75
## 94 1587 1 Female 51
## 289 154 2 Female 92
## 408 1221 3 Male 60
## 368 1548 1 Male 47
## 164 347 4 Female 82
## 413 61 2 Male 73
## 487 1876 2 Male 89
## 767 1085 2 Female 43
## 733 1944 1 Female 58
## 735 1577 1 Male 35
## 808 217 3 Male 80
## 458 120 3 Male 71
## 552 869 2 Male 63
## 53 1246 1 Female 72
## 409 23 1 Female 78
## 702 1163 2 Female 70
## 280 1501 4 Male 30
## 327 1608 2 Female 31
## 415 555 4 Female 33
## 83 1997 1 Male 72
## 396 426 3 Male 32
## 133 551 4 Female 88
## 275 254 3 Male 61
## 456 1740 2 Male 88
## 518 1768 2 Male 57
## 304 2008 2 Male 48
## 238 679 1 Female 92
## 507 485 4 Female 34
## 482 895 4 Male 48
## 476 62 1 Male 98
## 434 1762 4 Female 91
## 495 1880 2 Female 63
## 724 1073 4 Female 51
## 47 1734 1 Male 34
## 147 252 4 Female 72
## 331 1677 3 Male 36
## 218 1900 4 Female 58
## 555 287 4 Male 63
## 201 986 2 Female 100
## 587 107 2 Female 79
## 290 805 1 Female 72
## 467 1556 3 Male 70
## 393 1948 3 Male 48
## 255 1468 4 Male 55
## 558 649 2 Male 83
## 300 440 1 Male 84
## 253 834 2 Female 95
## 592 1489 1 Female 40
## 59 380 1 Female 80
## 811 239 3 Male 66
## 278 430 3 Female 33
## 137 816 2 Female 36
## 225 1940 2 Male 71
## 191 441 4 Female 64
## 544 271 4 Male 88
## 473 101 4 Male 52
## 523 86 2 Male 59
## 710 905 1 Male 98
## 861 1297 2 Female 40
## 182 1355 2 Female 31
## 852 41 4 Male 83
## 236 970 1 Male 46
## 779 1693 4 Male 76
## 187 1653 4 Male 77
## 717 1179 3 Male 72
## 740 652 1 Male 54
## 629 1096 4 Male 65
## 311 1580 2 Male 45
## 399 2026 3 Male 57
## 131 1669 1 Male 43
## 486 1 2 Female 94
## 370 439 1 Female 74
## 199 36 1 Female 42
## 783 910 4 Male 57
## 566 2020 2 Male 42
## 226 1989 4 Male 76
## 488 16 1 Male 31
## 614 959 4 Male 37
## 320 1269 2 Female 57
## 800 1664 4 Male 79
## 700 1217 4 Male 52
## 762 820 3 Female 33
## 684 223 3 Male 83
## 220 1275 3 Male 53
## 229 128 1 Female 33
## 11 1866 1 Female 81
## 342 1296 4 Male 52
## 716 1714 4 Male 58
## 65 1219 4 Female 97
## 259 1882 3 Male 64
## 540 1171 2 Male 45
## 24 1613 4 Male 54
## 134 1654 4 Male 66
## 590 75 3 Female 30
## 286 1549 2 Male 79
## 12 1858 3 Male 43
## 249 1392 4 Female 75
## 529 105 2 Male 30
## 215 700 1 Male 54
## 449 79 4 Female 50
## 20 1903 2 Male 78
## 530 161 2 Female 72
## 294 2000 3 Female 98
## 89 56 4 Male 37
## 435 954 1 Male 67
## 436 458 2 Female 56
## 330 1408 3 Female 31
## 639 639 2 Male 89
## 663 1732 3 Male 74
## 469 1037 2 Male 79
## 868 1503 3 Female 78
## 713 1258 4 Male 76
## 594 1617 3 Female 84
## 579 1633 1 Female 81
## 646 215 3 Male 67
## 633 1036 2 Male 42
## 581 1358 4 Male 67
## 513 1720 1 Male 56
## 619 2062 4 Male 42
## 282 957 4 Male 59
## 791 1509 3 Female 43
## 699 1624 2 Female 70
## 601 1495 4 Female 78
## 210 176 3 Male 96
## 172 1934 3 Male 64
## 22 712 2 Male 56
## 453 1797 3 Male 84
## 776 784 1 Male 79
## 626 1259 1 Male 76
## 252 990 1 Male 84
## 480 2017 3 Female 42
## 648 1718 4 Male 62
## 840 1867 4 Male 91
## 814 1625 4 Female 98
## 285 1152 3 Male 37
## 438 1952 3 Male 95
## 279 142 3 Male 46
## 203 830 3 Female 98
## 405 856 3 Female 53
## 138 1013 3 Female 56
## 217 634 3 Female 76
## 854 412 2 Male 68
## JobInvolvement JobLevel JobRole JobSatisfaction
## 834 3 1 Research Scientist 1
## 697 2 4 Manager 4
## 207 1 2 Sales Executive 3
## 715 2 3 Sales Executive 1
## 809 3 1 Research Scientist 4
## 725 3 1 Laboratory Technician 3
## 222 3 2 Sales Executive 1
## 71 3 1 Research Scientist 4
## 503 3 1 Sales Representative 3
## 403 3 1 Laboratory Technician 3
## 387 3 3 Sales Executive 3
## 806 3 3 Healthcare Representative 1
## 826 3 5 Research Director 4
## 821 4 1 Research Scientist 4
## 844 3 3 Healthcare Representative 4
## 528 3 2 Healthcare Representative 1
## 128 2 2 Manufacturing Director 4
## 539 3 1 Sales Representative 2
## 457 3 2 Sales Executive 4
## 732 3 2 Sales Executive 3
## 842 2 3 Human Resources 1
## 85 3 2 Manufacturing Director 2
## 122 3 1 Sales Representative 1
## 474 3 1 Research Scientist 4
## 819 2 1 Laboratory Technician 4
## 625 2 1 Laboratory Technician 1
## 184 3 3 Manufacturing Director 2
## 92 2 3 Sales Executive 1
## 269 3 2 Healthcare Representative 3
## 463 3 3 Sales Executive 4
## 407 2 2 Sales Executive 4
## 235 3 4 Manager 4
## 54 3 2 Laboratory Technician 2
## 146 3 2 Sales Executive 3
## 464 3 2 Manufacturing Director 4
## 192 3 3 Sales Executive 1
## 665 3 2 Sales Executive 3
## 394 2 3 Sales Executive 1
## 810 3 1 Research Scientist 2
## 512 3 3 Healthcare Representative 3
## 628 2 3 Research Director 4
## 248 2 1 Sales Representative 2
## 8 2 2 Sales Executive 4
## 468 4 1 Laboratory Technician 4
## 851 1 2 Sales Executive 3
## 404 3 3 Healthcare Representative 1
## 365 1 5 Manager 3
## 491 3 5 Research Director 1
## 616 2 2 Laboratory Technician 4
## 748 2 2 Healthcare Representative 1
## 123 3 3 Sales Executive 1
## 841 3 1 Laboratory Technician 3
## 326 3 1 Research Scientist 3
## 149 3 1 Laboratory Technician 2
## 91 3 1 Laboratory Technician 4
## 72 3 2 Sales Executive 1
## 563 3 1 Laboratory Technician 2
## 80 1 1 Laboratory Technician 4
## 828 3 2 Sales Executive 1
## 481 3 2 Healthcare Representative 1
## 466 3 1 Research Scientist 4
## 510 3 5 Research Director 3
## 381 3 5 Research Director 3
## 90 3 5 Manager 1
## 127 1 1 Research Scientist 1
## 737 2 4 Sales Executive 4
## 527 3 2 Sales Executive 1
## 100 2 1 Laboratory Technician 2
## 209 3 1 Research Scientist 1
## 148 3 2 Sales Executive 1
## 125 3 2 Sales Executive 4
## 754 3 3 Sales Executive 2
## 139 3 1 Laboratory Technician 1
## 649 2 2 Sales Executive 1
## 516 1 2 Manufacturing Director 2
## 3 3 3 Manufacturing Director 4
## 412 3 3 Sales Executive 3
## 667 3 3 Sales Executive 4
## 227 3 1 Laboratory Technician 4
## 4 3 3 Sales Executive 4
## 27 3 2 Sales Executive 2
## 240 2 3 Research Director 1
## 169 3 2 Research Scientist 1
## 422 3 2 Research Scientist 3
## 130 4 4 Sales Executive 1
## 784 3 2 Sales Executive 3
## 188 4 4 Manager 2
## 521 2 1 Research Scientist 4
## 745 3 2 Sales Executive 3
## 256 3 1 Research Scientist 3
## 145 4 2 Healthcare Representative 3
## 262 2 1 Research Scientist 1
## 369 4 1 Research Scientist 3
## 573 2 2 Sales Executive 4
## 135 2 2 Laboratory Technician 3
## 152 3 2 Sales Executive 4
## 374 3 1 Research Scientist 2
## 822 3 2 Sales Executive 4
## 785 3 2 Sales Executive 2
## 446 3 1 Research Scientist 4
## 793 2 3 Manufacturing Director 3
## 17 3 2 Manufacturing Director 1
## 718 3 2 Research Scientist 3
## 176 2 1 Research Scientist 2
## 319 2 2 Sales Executive 4
## 372 3 3 Sales Executive 1
## 461 3 1 Laboratory Technician 3
## 361 2 5 Manager 3
## 141 3 4 Manufacturing Director 2
## 28 3 2 Sales Executive 1
## 423 1 2 Sales Executive 3
## 363 1 5 Manager 4
## 796 3 1 Research Scientist 1
## 859 3 1 Laboratory Technician 1
## 295 3 4 Manager 4
## 574 4 2 Sales Executive 3
## 703 2 3 Sales Executive 4
## 334 3 3 Manufacturing Director 1
## 268 3 1 Laboratory Technician 2
## 272 3 2 Research Scientist 2
## 671 3 3 Research Director 3
## 51 3 1 Research Scientist 1
## 153 2 2 Sales Executive 3
## 441 3 2 Research Scientist 3
## 154 4 2 Healthcare Representative 2
## 87 3 2 Sales Executive 1
## 575 3 2 Sales Executive 2
## 414 3 2 Sales Executive 4
## 763 1 2 Sales Executive 2
## 58 3 1 Laboratory Technician 1
## 224 2 1 Research Scientist 3
## 580 3 2 Healthcare Representative 4
## 622 4 2 Manufacturing Director 3
## 155 3 1 Laboratory Technician 2
## 847 3 1 Laboratory Technician 2
## 542 4 1 Research Scientist 1
## 395 3 1 Research Scientist 4
## 67 1 2 Research Scientist 4
## 25 2 2 Healthcare Representative 4
## 288 3 1 Laboratory Technician 4
## 358 2 1 Research Scientist 2
## 827 3 3 Sales Executive 3
## 728 3 1 Research Scientist 3
## 730 3 1 Laboratory Technician 4
## 303 2 1 Research Scientist 2
## 69 3 1 Research Scientist 1
## 264 3 1 Sales Representative 4
## 562 3 4 Research Director 3
## 675 4 1 Research Scientist 3
## 427 3 1 Research Scientist 3
## 223 3 2 Sales Executive 4
## 35 2 2 Manufacturing Director 4
## 533 4 1 Research Scientist 3
## 239 3 1 Human Resources 2
## 243 3 1 Research Scientist 2
## 707 3 2 Research Scientist 4
## 43 3 1 Laboratory Technician 1
## 777 3 1 Human Resources 3
## 419 2 2 Manufacturing Director 4
## 333 3 1 Research Scientist 2
## 640 3 1 Sales Representative 1
## 757 3 2 Healthcare Representative 4
## 774 2 1 Research Scientist 3
## 864 3 4 Sales Executive 3
## 499 3 2 Healthcare Representative 3
## 484 3 1 Research Scientist 1
## 281 3 2 Sales Executive 4
## 84 4 2 Research Scientist 1
## 761 2 1 Research Scientist 1
## 37 2 3 Sales Executive 1
## 292 3 2 Laboratory Technician 2
## 537 3 3 Sales Executive 2
## 118 2 2 Laboratory Technician 2
## 261 2 1 Sales Representative 4
## 213 4 1 Research Scientist 4
## 459 3 1 Research Scientist 1
## 593 3 1 Sales Representative 3
## 448 4 2 Healthcare Representative 1
## 26 3 5 Manager 2
## 599 4 2 Manufacturing Director 2
## 113 3 3 Healthcare Representative 2
## 345 3 2 Sales Executive 1
## 870 3 2 Laboratory Technician 2
## 177 3 5 Manager 1
## 156 3 2 Sales Executive 3
## 568 2 2 Sales Executive 1
## 673 3 2 Sales Executive 1
## 432 3 1 Research Scientist 3
## 759 3 1 Laboratory Technician 3
## 440 4 2 Healthcare Representative 4
## 347 3 2 Manufacturing Director 3
## 833 2 1 Laboratory Technician 1
## 274 2 2 Sales Executive 1
## 40 4 1 Sales Representative 4
## 636 2 1 Laboratory Technician 4
## 2 2 5 Research Director 3
## 216 3 1 Research Scientist 3
## 312 3 4 Manufacturing Director 3
## 305 3 1 Research Scientist 3
## 838 4 2 Sales Executive 4
## 686 3 3 Manufacturing Director 2
## 162 2 3 Healthcare Representative 1
## 866 3 3 Manufacturing Director 4
## 832 3 1 Sales Representative 3
## 627 3 2 Sales Executive 3
## 329 2 2 Research Scientist 2
## 179 3 3 Sales Executive 4
## 545 2 1 Research Scientist 1
## 397 3 4 Research Director 3
## 185 3 1 Laboratory Technician 3
## 357 2 1 Research Scientist 2
## 170 3 1 Research Scientist 4
## 251 2 1 Laboratory Technician 3
## 638 3 3 Sales Executive 3
## 869 3 1 Research Scientist 3
## 429 2 1 Laboratory Technician 3
## 57 4 1 Research Scientist 3
## 705 3 1 Human Resources 3
## 55 3 2 Manufacturing Director 4
## 853 2 2 Laboratory Technician 3
## 478 3 2 Sales Executive 3
## 797 3 4 Healthcare Representative 4
## 770 2 2 Sales Executive 3
## 257 3 1 Sales Representative 2
## 79 2 3 Sales Executive 4
## 66 4 1 Research Scientist 2
## 687 3 3 Healthcare Representative 4
## 339 3 2 Sales Executive 3
## 631 2 1 Research Scientist 4
## 676 2 5 Manager 2
## 143 3 3 Manufacturing Director 3
## 561 3 1 Laboratory Technician 2
## 254 4 1 Human Resources 1
## 189 3 5 Research Director 2
## 1 3 2 Sales Executive 4
## 858 2 3 Manufacturing Director 3
## 578 2 1 Laboratory Technician 3
## 234 3 2 Sales Executive 1
## 751 3 1 Sales Representative 4
## 299 2 1 Research Scientist 1
## 543 2 1 Research Scientist 3
## 214 2 2 Manufacturing Director 4
## 366 4 4 Sales Executive 1
## 624 3 3 Human Resources 4
## 610 2 1 Sales Representative 4
## 250 3 4 Manager 1
## 119 3 1 Research Scientist 3
## 103 3 2 Sales Executive 3
## 221 3 2 Sales Executive 4
## 630 2 3 Healthcare Representative 2
## 605 1 1 Sales Representative 2
## 462 3 1 Laboratory Technician 2
## 714 2 3 Sales Executive 4
## 517 2 1 Sales Representative 2
## 287 3 1 Research Scientist 2
## 519 2 2 Laboratory Technician 4
## 674 3 1 Research Scientist 2
## 392 2 2 Sales Executive 4
## 758 3 2 Healthcare Representative 3
## 514 3 3 Research Director 4
## 668 1 1 Laboratory Technician 4
## 317 3 1 Sales Representative 3
## 52 3 5 Research Director 1
## 511 3 2 Sales Executive 4
## 815 2 1 Sales Representative 2
## 341 2 1 Research Scientist 1
## 612 2 2 Healthcare Representative 3
## 564 3 3 Research Director 3
## 183 4 1 Research Scientist 3
## 97 3 4 Manager 2
## 602 3 2 Research Scientist 1
## 711 3 3 Manager 1
## 586 3 2 Laboratory Technician 4
## 642 3 2 Healthcare Representative 1
## 651 2 1 Laboratory Technician 1
## 666 4 2 Healthcare Representative 3
## 508 3 3 Sales Executive 2
## 70 3 1 Research Scientist 4
## 73 2 2 Manufacturing Director 3
## 509 3 1 Sales Representative 4
## 704 3 1 Human Resources 2
## 455 2 4 Manager 1
## 662 2 1 Research Scientist 3
## 7 4 1 Research Scientist 3
## 198 2 4 Sales Executive 2
## 442 3 1 Laboratory Technician 3
## 756 1 1 Laboratory Technician 3
## 322 3 1 Research Scientist 4
## 265 3 1 Laboratory Technician 3
## 150 1 2 Manufacturing Director 1
## 232 3 1 Sales Representative 4
## 421 3 2 Manufacturing Director 2
## 231 2 2 Manufacturing Director 4
## 786 3 4 Research Director 2
## 241 3 3 Manufacturing Director 1
## 228 4 1 Research Scientist 2
## 284 3 2 Sales Executive 4
## 99 3 1 Laboratory Technician 2
## 181 2 3 Manufacturing Director 2
## 496 3 4 Manager 3
## 39 3 5 Manager 2
## 212 3 5 Manager 3
## 107 3 2 Manufacturing Director 3
## 325 2 4 Manufacturing Director 4
## 635 3 3 Healthcare Representative 1
## 343 2 3 Healthcare Representative 4
## 237 3 5 Manager 3
## 206 3 1 Research Scientist 1
## 607 2 2 Sales Executive 3
## 318 2 2 Healthcare Representative 3
## 772 3 3 Sales Executive 1
## 645 2 1 Research Scientist 1
## 749 3 1 Laboratory Technician 1
## 385 3 1 Research Scientist 3
## 794 3 3 Healthcare Representative 3
## 313 3 1 Research Scientist 3
## 380 3 1 Research Scientist 3
## 260 3 1 Laboratory Technician 3
## 10 2 2 Healthcare Representative 3
## 447 3 2 Research Scientist 4
## 534 3 2 Healthcare Representative 4
## 497 3 1 Research Scientist 4
## 352 4 2 Sales Executive 4
## 746 2 1 Laboratory Technician 1
## 158 3 4 Healthcare Representative 4
## 483 2 2 Research Scientist 4
## 323 2 1 Research Scientist 4
## 324 2 2 Research Scientist 1
## 535 1 1 Laboratory Technician 1
## 307 3 1 Laboratory Technician 4
## 694 3 1 Research Scientist 4
## 750 3 1 Research Scientist 4
## 16 3 2 Sales Executive 3
## 855 2 2 Manufacturing Director 2
## 823 2 2 Sales Executive 3
## 591 2 1 Research Scientist 3
## 360 3 1 Laboratory Technician 4
## 470 4 2 Manufacturing Director 1
## 68 3 1 Laboratory Technician 4
## 548 2 2 Laboratory Technician 3
## 670 3 4 Research Director 4
## 424 3 1 Laboratory Technician 4
## 121 4 3 Research Director 1
## 818 3 3 Research Director 3
## 258 3 2 Sales Executive 1
## 696 2 1 Sales Representative 4
## 812 3 1 Laboratory Technician 4
## 479 3 1 Laboratory Technician 1
## 490 3 2 Healthcare Representative 3
## 867 3 2 Sales Executive 4
## 471 3 1 Research Scientist 2
## 862 2 3 Sales Executive 4
## 824 3 1 Laboratory Technician 3
## 522 3 1 Laboratory Technician 2
## 401 1 2 Manufacturing Director 4
## 353 2 2 Research Scientist 4
## 163 3 2 Sales Executive 3
## 492 3 4 Research Director 1
## 820 3 2 Sales Executive 3
## 632 3 3 Sales Executive 4
## 384 3 1 Sales Representative 1
## 644 1 1 Research Scientist 1
## 792 3 1 Research Scientist 2
## 525 2 1 Research Scientist 4
## 677 3 2 Manufacturing Director 2
## 807 4 2 Sales Executive 3
## 825 3 2 Manufacturing Director 3
## 551 2 5 Manager 3
## 328 3 2 Research Scientist 1
## 356 1 2 Sales Executive 1
## 77 2 3 Sales Executive 3
## 116 3 3 Sales Executive 2
## 450 2 2 Sales Executive 1
## 701 2 1 Human Resources 2
## 443 3 1 Laboratory Technician 3
## 739 3 2 Sales Executive 3
## 835 2 4 Healthcare Representative 2
## 160 3 3 Research Director 1
## 850 4 1 Laboratory Technician 4
## 604 3 1 Laboratory Technician 4
## 165 3 3 Sales Executive 2
## 606 4 4 Research Director 3
## 524 2 5 Research Director 3
## 126 3 2 Sales Executive 4
## 93 2 1 Laboratory Technician 3
## 418 4 1 Laboratory Technician 4
## 64 3 1 Research Scientist 3
## 685 3 1 Research Scientist 4
## 293 3 1 Research Scientist 2
## 727 3 1 Sales Representative 4
## 729 1 1 Laboratory Technician 4
## 860 3 3 Healthcare Representative 4
## 609 4 1 Laboratory Technician 1
## 246 2 1 Laboratory Technician 4
## 375 3 1 Sales Representative 1
## 159 3 3 Manufacturing Director 1
## 108 4 5 Manager 2
## 489 2 3 Laboratory Technician 3
## 391 3 2 Sales Executive 1
## 82 3 2 Manufacturing Director 3
## 379 1 1 Research Scientist 4
## 439 2 1 Sales Representative 3
## 681 2 2 Sales Executive 1
## 477 2 2 Sales Executive 3
## 314 2 2 Manufacturing Director 3
## 843 2 4 Manufacturing Director 4
## 863 4 2 Sales Executive 4
## 839 2 3 Manufacturing Director 2
## 362 4 1 Sales Representative 1
## 660 2 2 Manufacturing Director 2
## 643 2 4 Manager 1
## 310 4 2 Sales Executive 2
## 45 3 1 Research Scientist 3
## 390 2 1 Sales Representative 2
## 193 3 2 Manufacturing Director 3
## 494 2 3 Healthcare Representative 2
## 34 2 1 Laboratory Technician 3
## 78 4 4 Manager 1
## 344 3 1 Research Scientist 3
## 753 3 4 Healthcare Representative 3
## 202 3 2 Research Scientist 2
## 6 3 3 Manufacturing Director 1
## 36 4 1 Laboratory Technician 2
## 849 2 2 Sales Executive 3
## 340 3 1 Laboratory Technician 1
## 132 3 2 Sales Executive 4
## 373 3 3 Laboratory Technician 1
## 747 2 1 Human Resources 1
## 44 2 5 Manager 4
## 267 3 4 Research Director 1
## 410 3 4 Research Director 1
## 760 1 5 Manager 2
## 788 2 2 Research Scientist 2
## 596 4 2 Sales Executive 1
## 302 3 3 Sales Executive 1
## 731 3 2 Manufacturing Director 2
## 56 3 1 Laboratory Technician 1
## 454 3 2 Sales Executive 3
## 382 3 1 Human Resources 3
## 647 3 1 Laboratory Technician 3
## 445 3 5 Research Director 4
## 804 3 3 Sales Executive 1
## 104 4 1 Research Scientist 1
## 437 3 1 Research Scientist 3
## 656 3 2 Healthcare Representative 1
## 679 3 2 Manufacturing Director 2
## 752 3 1 Sales Representative 2
## 426 2 3 Sales Executive 4
## 296 3 1 Laboratory Technician 3
## 420 2 3 Research Director 3
## 830 3 5 Manager 2
## 32 4 1 Laboratory Technician 2
## 726 3 2 Manufacturing Director 3
## 166 2 1 Sales Representative 3
## 247 3 3 Manufacturing Director 4
## 117 3 2 Sales Executive 4
## 845 1 2 Sales Executive 4
## 364 3 5 Research Director 3
## 557 3 2 Laboratory Technician 3
## 376 1 4 Research Director 4
## 98 2 2 Healthcare Representative 4
## 502 2 1 Laboratory Technician 3
## 736 4 2 Sales Executive 3
## 180 2 2 Sales Executive 4
## 338 3 2 Healthcare Representative 4
## 501 2 3 Manufacturing Director 1
## 778 3 1 Sales Representative 1
## 531 2 3 Healthcare Representative 3
## 744 3 1 Laboratory Technician 2
## 196 3 1 Research Scientist 4
## 30 3 1 Research Scientist 4
## 31 1 2 Sales Executive 4
## 400 3 3 Manufacturing Director 3
## 95 3 5 Manager 2
## 49 3 1 Sales Representative 2
## 771 2 2 Sales Executive 4
## 659 2 2 Manufacturing Director 2
## 465 2 1 Research Scientist 3
## 698 3 2 Laboratory Technician 2
## 798 3 1 Laboratory Technician 1
## 782 2 2 Healthcare Representative 3
## 291 3 1 Laboratory Technician 4
## 764 3 2 Sales Executive 3
## 211 2 4 Manufacturing Director 2
## 634 3 1 Laboratory Technician 4
## 654 3 3 Manager 3
## 775 3 3 Healthcare Representative 4
## 559 3 2 Manufacturing Director 2
## 805 3 2 Laboratory Technician 2
## 178 3 1 Research Scientist 1
## 110 3 1 Research Scientist 1
## 857 3 1 Research Scientist 1
## 337 3 2 Sales Executive 4
## 115 3 2 Sales Executive 1
## 585 3 2 Healthcare Representative 4
## 460 2 2 Laboratory Technician 4
## 505 3 4 Research Director 3
## 576 2 2 Manufacturing Director 3
## 799 2 1 Research Scientist 2
## 856 3 1 Sales Representative 3
## 233 1 4 Sales Executive 3
## 780 3 1 Research Scientist 4
## 129 2 2 Research Scientist 3
## 837 3 1 Research Scientist 3
## 766 1 2 Laboratory Technician 1
## 569 2 2 Sales Executive 3
## 388 3 2 Manufacturing Director 4
## 321 2 1 Laboratory Technician 2
## 308 2 1 Research Scientist 4
## 829 2 1 Laboratory Technician 2
## 94 3 2 Healthcare Representative 3
## 289 3 3 Sales Executive 4
## 408 1 1 Laboratory Technician 3
## 368 3 2 Sales Executive 1
## 164 4 2 Research Scientist 1
## 413 3 1 Research Scientist 2
## 487 3 1 Sales Representative 4
## 767 4 1 Research Scientist 3
## 733 2 1 Human Resources 2
## 735 3 1 Research Scientist 4
## 808 3 1 Research Scientist 4
## 458 3 2 Sales Executive 4
## 552 2 1 Human Resources 4
## 53 4 1 Research Scientist 4
## 409 2 4 Manager 4
## 702 3 2 Sales Executive 3
## 280 3 2 Healthcare Representative 3
## 327 3 5 Research Director 4
## 415 1 1 Sales Representative 3
## 83 2 2 Manufacturing Director 3
## 396 3 1 Research Scientist 4
## 133 2 2 Healthcare Representative 4
## 275 4 5 Research Director 2
## 456 3 5 Manager 2
## 518 3 1 Laboratory Technician 2
## 304 3 2 Research Scientist 3
## 238 3 2 Research Scientist 4
## 507 3 1 Sales Representative 3
## 482 3 1 Research Scientist 4
## 476 3 2 Sales Executive 4
## 434 2 1 Research Scientist 1
## 495 2 2 Sales Executive 3
## 724 2 3 Manufacturing Director 4
## 47 1 2 Sales Executive 2
## 147 3 1 Research Scientist 2
## 331 3 4 Manager 3
## 218 3 4 Manager 4
## 555 2 2 Healthcare Representative 4
## 201 4 4 Sales Executive 2
## 587 1 2 Research Scientist 4
## 290 3 1 Sales Representative 4
## 467 3 1 Sales Representative 4
## 393 3 1 Research Scientist 1
## 255 2 3 Research Director 2
## 558 2 1 Research Scientist 4
## 300 1 1 Research Scientist 4
## 253 2 1 Research Scientist 3
## 592 2 2 Sales Executive 2
## 59 3 3 Research Director 4
## 811 3 2 Laboratory Technician 1
## 278 3 1 Laboratory Technician 4
## 137 2 1 Laboratory Technician 1
## 225 3 1 Research Scientist 4
## 191 3 2 Research Scientist 4
## 544 3 2 Manufacturing Director 3
## 473 3 3 Research Director 1
## 523 3 3 Research Scientist 1
## 710 3 4 Manager 1
## 861 2 1 Laboratory Technician 3
## 182 3 2 Sales Executive 1
## 852 2 1 Laboratory Technician 3
## 236 2 3 Sales Executive 3
## 779 3 1 Laboratory Technician 4
## 187 4 2 Manufacturing Director 3
## 717 2 2 Sales Executive 3
## 740 2 2 Laboratory Technician 4
## 629 3 3 Manufacturing Director 2
## 311 3 2 Research Scientist 4
## 399 3 2 Healthcare Representative 3
## 131 2 2 Sales Executive 2
## 486 3 2 Sales Executive 4
## 370 4 2 Research Scientist 4
## 199 2 3 Healthcare Representative 4
## 783 3 1 Human Resources 2
## 566 3 1 Research Scientist 4
## 226 3 1 Laboratory Technician 2
## 488 3 1 Research Scientist 3
## 614 2 1 Sales Representative 2
## 320 4 1 Research Scientist 4
## 800 4 2 Healthcare Representative 1
## 700 3 5 Research Director 2
## 762 3 4 Manager 4
## 684 4 3 Healthcare Representative 4
## 220 3 1 Research Scientist 4
## 229 3 2 Sales Executive 3
## 11 2 5 Manager 3
## 342 2 1 Laboratory Technician 4
## 716 1 1 Human Resources 3
## 65 3 1 Laboratory Technician 2
## 259 3 3 Sales Executive 4
## 540 3 2 Sales Executive 1
## 24 3 2 Sales Executive 1
## 134 3 2 Manufacturing Director 2
## 590 3 1 Laboratory Technician 1
## 286 3 2 Laboratory Technician 3
## 12 3 1 Human Resources 4
## 249 3 2 Research Scientist 4
## 529 2 1 Research Scientist 3
## 215 2 1 Research Scientist 4
## 449 3 2 Laboratory Technician 4
## 20 4 2 Healthcare Representative 1
## 530 3 1 Research Scientist 2
## 294 3 3 Research Director 2
## 89 3 3 Sales Executive 3
## 435 2 3 Sales Executive 2
## 436 3 2 Sales Executive 2
## 330 3 4 Manager 1
## 639 4 1 Research Scientist 4
## 663 3 3 Sales Executive 4
## 469 1 2 Sales Executive 1
## 868 3 2 Sales Executive 3
## 713 4 1 Laboratory Technician 2
## 594 3 1 Laboratory Technician 1
## 579 3 2 Manufacturing Director 3
## 646 2 3 Sales Executive 4
## 633 3 2 Sales Executive 4
## 581 3 1 Laboratory Technician 4
## 513 3 1 Research Scientist 3
## 619 2 3 Healthcare Representative 1
## 282 3 1 Laboratory Technician 1
## 791 2 2 Laboratory Technician 1
## 699 3 1 Sales Representative 4
## 601 3 1 Sales Representative 2
## 210 3 3 Sales Executive 3
## 172 3 2 Research Scientist 2
## 22 4 2 Sales Executive 2
## 453 3 2 Sales Executive 4
## 776 4 1 Research Scientist 3
## 626 3 1 Research Scientist 3
## 252 3 2 Sales Executive 4
## 480 4 3 Research Director 4
## 648 1 1 Laboratory Technician 3
## 840 2 5 Research Director 2
## 814 3 5 Manager 3
## 285 4 1 Human Resources 3
## 438 3 1 Laboratory Technician 1
## 279 2 2 Sales Executive 3
## 203 2 2 Manufacturing Director 3
## 405 2 1 Research Scientist 1
## 138 3 3 Manufacturing Director 3
## 217 3 2 Healthcare Representative 3
## 854 4 2 Healthcare Representative 1
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## 834 Single 2552 7172 1 Y
## 697 Married 15202 5602 2 Y
## 207 Single 5346 9489 8 Y
## 715 Single 7587 14229 1 Y
## 809 Married 2862 3811 1 Y
## 725 Single 3968 13624 4 Y
## 222 Married 4286 5630 2 Y
## 71 Married 2782 21412 3 Y
## 503 Married 2655 11740 2 Y
## 403 Single 2105 5411 4 Y
## 387 Single 8789 9096 1 Y
## 806 Married 8321 25949 7 Y
## 826 Divorced 19627 21445 9 Y
## 821 Married 3312 18783 3 Y
## 844 Divorced 8500 5494 0 Y
## 528 Married 6833 17089 1 Y
## 128 Married 5906 23888 0 Y
## 539 Single 3202 21972 1 Y
## 457 Married 5476 22589 1 Y
## 732 Married 5238 17778 4 Y
## 842 Single 7988 9769 1 Y
## 85 Single 5410 2323 9 Y
## 122 Single 2380 25479 1 Y
## 474 Single 3452 9752 5 Y
## 819 Single 2720 11162 0 Y
## 625 Single 2561 5355 7 Y
## 184 Single 8376 9150 4 Y
## 92 Married 7336 11162 1 Y
## 269 Divorced 5968 18079 1 Y
## 463 Single 9208 6645 4 Y
## 407 Married 5468 13402 1 Y
## 235 Married 16959 19494 1 Y
## 54 Married 4447 23163 1 Y
## 146 Married 4649 16928 1 Y
## 464 Single 5238 6670 2 Y
## 192 Married 9637 8277 2 Y
## 665 Single 4568 10034 0 Y
## 394 Married 10266 2845 4 Y
## 810 Married 3058 13364 0 Y
## 512 Married 10920 3449 3 Y
## 628 Divorced 11510 15682 0 Y
## 248 Married 2269 18024 0 Y
## 8 Divorced 6694 24223 2 Y
## 468 Married 3038 22002 3 Y
## 851 Married 4559 24788 3 Y
## 404 Married 8008 22792 4 Y
## 365 Married 19187 6992 4 Y
## 491 Single 18665 25594 9 Y
## 616 Married 3986 11912 1 Y
## 748 Single 5343 25755 0 Y
## 123 Divorced 10761 19239 4 Y
## 841 Single 1420 25233 1 Y
## 326 Single 2318 17808 1 Y
## 149 Single 3597 6409 8 Y
## 91 Married 1702 12106 1 Y
## 72 Single 8463 23490 0 Y
## 563 Married 2811 12086 9 Y
## 80 Divorced 1129 17536 1 Y
## 828 Single 4538 6039 0 Y
## 481 Married 5373 6225 0 Y
## 466 Single 3944 4306 5 Y
## 510 Married 19545 16280 1 Y
## 381 Married 18300 16375 4 Y
## 90 Single 18947 22822 3 Y
## 127 Single 3919 4681 1 Y
## 737 Married 12031 8828 0 Y
## 527 Divorced 5006 6319 4 Y
## 100 Married 3294 3708 5 Y
## 209 Divorced 3204 10415 5 Y
## 148 Married 6392 10589 2 Y
## 125 Single 4554 20260 1 Y
## 754 Married 7314 14011 5 Y
## 139 Married 2404 4303 7 Y
## 649 Single 4312 23016 0 Y
## 516 Divorced 5482 16321 5 Y
## 3 Single 9362 19944 2 Y
## 412 Single 9241 15869 1 Y
## 667 Married 7918 6599 1 Y
## 227 Divorced 1951 10910 1 Y
## 4 Married 10422 24032 1 Y
## 27 Married 9924 12355 0 Y
## 240 Divorced 13269 21981 5 Y
## 169 Divorced 5577 22087 3 Y
## 422 Married 4449 23866 3 Y
## 130 Divorced 13225 7739 2 Y
## 784 Married 4539 4905 1 Y
## 188 Divorced 15972 21086 6 Y
## 521 Single 2553 8306 1 Y
## 745 Divorced 4907 13684 0 Y
## 256 Single 2438 24978 4 Y
## 145 Divorced 6377 13888 5 Y
## 262 Single 2362 7568 6 Y
## 369 Married 2073 12826 2 Y
## 573 Married 6834 19255 1 Y
## 135 Divorced 5257 6227 1 Y
## 152 Single 5396 21703 1 Y
## 374 Married 3936 9953 1 Y
## 822 Divorced 6931 10732 2 Y
## 785 Married 4978 3536 7 Y
## 446 Married 3420 10205 7 Y
## 793 Married 7756 14199 3 Y
## 17 Married 6725 13554 1 Y
## 718 Single 3660 7909 3 Y
## 176 Single 2070 25326 1 Y
## 319 Married 6120 3567 3 Y
## 372 Married 8412 2890 0 Y
## 461 Married 2093 9260 4 Y
## 361 Married 19999 5678 0 Y
## 141 Married 13402 18235 4 Y
## 28 Single 8224 18385 0 Y
## 423 Married 6172 20739 4 Y
## 363 Married 19845 25846 1 Y
## 796 Single 3348 3164 1 Y
## 859 Married 2238 6961 2 Y
## 295 Single 16437 17381 1 Y
## 574 Married 6799 22128 1 Y
## 703 Married 10377 13755 4 Y
## 334 Married 7412 6009 1 Y
## 268 Single 1393 24852 1 Y
## 272 Divorced 5433 19332 1 Y
## 671 Single 13664 25258 4 Y
## 51 Single 2619 14561 3 Y
## 153 Married 5484 16985 1 Y
## 441 Single 4998 2338 4 Y
## 154 Single 6870 15530 3 Y
## 87 Single 4960 11825 2 Y
## 575 Married 6272 7428 1 Y
## 414 Single 4648 26075 8 Y
## 763 Single 6151 22074 1 Y
## 58 Single 4723 16213 1 Y
## 224 Divorced 1563 12530 1 Y
## 580 Divorced 6623 4204 1 Y
## 622 Divorced 5067 6759 1 Y
## 155 Divorced 2008 6896 1 Y
## 847 Single 1483 16102 1 Y
## 542 Married 3143 6076 6 Y
## 395 Single 2515 9068 5 Y
## 67 Single 6646 19368 1 Y
## 25 Single 6949 12291 0 Y
## 288 Divorced 2661 8758 0 Y
## 358 Married 2546 18300 5 Y
## 827 Married 8161 19002 2 Y
## 728 Divorced 3815 5972 1 Y
## 730 Married 2523 19299 0 Y
## 303 Divorced 3537 23737 5 Y
## 69 Married 2194 5868 4 Y
## 264 Married 2157 18203 1 Y
## 562 Married 15787 21624 2 Y
## 675 Married 3517 22490 7 Y
## 427 Divorced 1274 7152 1 Y
## 223 Divorced 5368 16130 1 Y
## 35 Single 9679 10138 8 Y
## 533 Married 2700 23779 1 Y
## 239 Married 2187 19655 4 Y
## 243 Married 2703 4956 0 Y
## 707 Married 6854 15696 4 Y
## 43 Divorced 2587 24941 4 Y
## 777 Divorced 2064 15428 0 Y
## 419 Married 6474 9961 1 Y
## 333 Divorced 2342 11092 1 Y
## 640 Divorced 3041 16346 0 Y
## 757 Married 4014 19170 1 Y
## 774 Married 3388 21777 0 Y
## 864 Divorced 13770 10225 9 Y
## 499 Married 5237 16577 6 Y
## 484 Divorced 4257 13939 4 Y
## 281 Married 7847 6069 1 Y
## 84 Divorced 5974 17001 4 Y
## 761 Single 2404 11479 6 Y
## 37 Married 10306 21530 9 Y
## 292 Married 5674 6927 7 Y
## 537 Single 7351 20619 7 Y
## 118 Married 4025 23679 4 Y
## 261 Single 2785 11882 7 Y
## 213 Single 4680 15232 3 Y
## 459 Married 2066 10494 2 Y
## 593 Married 2909 15747 3 Y
## 448 Married 5538 5696 5 Y
## 26 Married 18213 8751 7 Y
## 599 Married 6142 5174 1 Y
## 113 Single 10445 15322 7 Y
## 345 Divorced 4115 13192 8 Y
## 870 Single 4425 15986 5 Y
## 177 Married 19859 21199 5 Y
## 156 Married 4736 6069 7 Y
## 568 Married 9714 5323 1 Y
## 673 Single 4898 11827 0 Y
## 432 Divorced 3544 8532 9 Y
## 759 Married 3306 26176 7 Y
## 440 Single 7625 19383 0 Y
## 347 Single 4033 15834 2 Y
## 833 Married 3196 12449 1 Y
## 274 Married 5647 13494 4 Y
## 40 Married 2791 21981 0 Y
## 636 Married 2376 26537 1 Y
## 2 Single 19626 17544 1 Y
## 216 Divorced 2345 8045 2 Y
## 312 Single 13237 20364 7 Y
## 305 Single 2479 26227 4 Y
## 838 Single 5337 19921 1 Y
## 686 Divorced 10648 14394 1 Y
## 162 Single 8722 12355 1 Y
## 866 Single 7898 18706 1 Y
## 832 Single 1675 26820 1 Y
## 627 Married 4162 15211 1 Y
## 329 Divorced 2133 18115 1 Y
## 179 Single 8998 15589 1 Y
## 545 Single 4381 7530 1 Y
## 397 Married 17861 2288 6 Y
## 185 Married 3755 17872 1 Y
## 357 Married 2368 23300 1 Y
## 170 Divorced 2514 26968 4 Y
## 251 Divorced 2008 20439 1 Y
## 638 Divorced 8628 22914 1 Y
## 869 Married 4477 20100 4 Y
## 429 Single 3730 9571 0 Y
## 57 Divorced 2759 20366 6 Y
## 705 Married 2267 25657 8 Y
## 55 Single 4424 20682 1 Y
## 853 Single 4193 12682 0 Y
## 478 Divorced 4306 4267 5 Y
## 797 Married 13496 7501 0 Y
## 770 Married 5593 17970 1 Y
## 257 Divorced 2789 3909 1 Y
## 79 Single 8865 16840 6 Y
## 66 Divorced 3298 15053 0 Y
## 687 Single 7553 22930 0 Y
## 339 Single 4728 17251 3 Y
## 631 Married 2662 7975 8 Y
## 676 Married 18789 9946 2 Y
## 143 Divorced 10845 24208 6 Y
## 561 Single 2088 15062 4 Y
## 254 Married 2956 21495 0 Y
## 189 Divorced 19144 15815 3 Y
## 1 Divorced 4403 9250 2 Y
## 858 Married 8943 14034 1 Y
## 578 Single 3162 7973 3 Y
## 234 Married 5940 17011 2 Y
## 751 Single 2323 17205 1 Y
## 299 Single 2321 10322 0 Y
## 543 Divorced 2566 25326 1 Y
## 214 Married 6062 4051 9 Y
## 366 Married 13212 18256 9 Y
## 624 Single 8837 16642 1 Y
## 610 Single 2342 21437 0 Y
## 250 Married 16595 5626 7 Y
## 119 Divorced 2451 4609 4 Y
## 103 Married 6538 12740 9 Y
## 221 Single 6274 18686 1 Y
## 630 Single 8621 17654 1 Y
## 605 Single 2760 14630 1 Y
## 462 Single 2013 10950 2 Y
## 714 Married 9699 7246 4 Y
## 517 Married 2413 18798 1 Y
## 287 Single 2472 26092 1 Y
## 519 Married 4627 23631 0 Y
## 674 Divorced 2022 16612 1 Y
## 392 Married 4157 21436 7 Y
## 758 Married 6651 21534 2 Y
## 514 Single 13458 15146 1 Y
## 668 Divorced 3408 6705 7 Y
## 317 Married 2231 11314 6 Y
## 52 Married 19701 22456 3 Y
## 511 Married 5343 5982 1 Y
## 815 Married 2899 12102 0 Y
## 341 Married 3419 13072 9 Y
## 612 Single 6811 2112 2 Y
## 564 Married 11031 26862 4 Y
## 183 Single 4739 16090 4 Y
## 97 Married 16606 11380 8 Y
## 602 Married 4087 25174 4 Y
## 711 Married 11916 25927 1 Y
## 586 Married 2741 16523 8 Y
## 642 Divorced 4448 10748 2 Y
## 651 Single 2450 21731 1 Y
## 666 Single 6294 23060 8 Y
## 508 Married 10596 15395 2 Y
## 70 Single 2115 15881 1 Y
## 73 Married 5916 15497 3 Y
## 509 Divorced 2793 2539 4 Y
## 704 Married 2148 6889 0 Y
## 455 Married 15402 17997 7 Y
## 662 Divorced 2768 8416 3 Y
## 7 Married 2127 5561 2 Y
## 198 Married 13120 11879 6 Y
## 442 Married 2974 25412 9 Y
## 756 Single 3708 2104 2 Y
## 322 Married 2691 7660 1 Y
## 265 Married 2774 13257 0 Y
## 150 Divorced 4877 20460 0 Y
## 232 Married 2014 9687 1 Y
## 421 Married 4325 17736 1 Y
## 231 Divorced 5770 5388 1 Y
## 786 Single 16422 8847 3 Y
## 241 Married 7406 6950 1 Y
## 228 Divorced 2328 12392 1 Y
## 284 Divorced 7547 7143 4 Y
## 99 Married 3180 4668 0 Y
## 181 Divorced 10274 19588 2 Y
## 496 Married 16307 5594 2 Y
## 39 Single 19717 4022 6 Y
## 212 Married 18303 7770 6 Y
## 107 Married 4319 26283 1 Y
## 325 Married 12742 7060 1 Y
## 635 Married 9985 9262 8 Y
## 343 Divorced 7119 21214 4 Y
## 237 Married 18880 17312 5 Y
## 206 Married 2559 7508 1 Y
## 607 Single 5473 24668 7 Y
## 318 Single 4617 14120 1 Y
## 772 Divorced 10453 2137 1 Y
## 645 Divorced 2107 20293 6 Y
## 749 Divorced 3907 3622 1 Y
## 385 Married 2461 10332 9 Y
## 794 Divorced 10096 8202 1 Y
## 313 Single 4084 4156 1 Y
## 380 Married 2132 11539 4 Y
## 260 Divorced 3977 7298 6 Y
## 10 Married 5063 15332 1 Y
## 447 Married 4735 9867 7 Y
## 534 Divorced 6667 16542 5 Y
## 497 Married 2654 9655 3 Y
## 352 Married 6929 12241 4 Y
## 746 Divorced 2296 10036 0 Y
## 158 Divorced 12169 13547 7 Y
## 483 Married 5747 26496 1 Y
## 323 Divorced 3761 2373 9 Y
## 324 Divorced 4615 21029 8 Y
## 535 Married 2119 4759 1 Y
## 307 Married 2380 20165 4 Y
## 694 Single 2285 3427 9 Y
## 750 Single 2500 10515 0 Y
## 16 Married 6932 24406 1 Y
## 855 Married 4788 25388 0 Y
## 823 Married 6712 8978 1 Y
## 591 Married 2814 10293 1 Y
## 360 Married 3210 20251 0 Y
## 470 Divorced 4450 26250 1 Y
## 68 Single 2647 13672 1 Y
## 548 Single 5094 11983 6 Y
## 670 Married 17779 23474 3 Y
## 424 Single 1611 19305 1 Y
## 121 Married 11713 20335 9 Y
## 818 Married 11691 25995 0 Y
## 258 Divorced 4779 12761 7 Y
## 696 Single 3294 13137 1 Y
## 812 Married 3229 4910 4 Y
## 479 Married 2950 17363 9 Y
## 490 Single 6142 7360 3 Y
## 867 Married 4465 12069 0 Y
## 471 Married 2226 6073 1 Y
## 862 Single 10231 20364 3 Y
## 824 Married 2610 6233 1 Y
## 522 Married 2408 7324 1 Y
## 401 Single 6091 24793 2 Y
## 353 Married 4422 21203 3 Y
## 163 Divorced 7104 20431 0 Y
## 492 Married 16880 22422 4 Y
## 820 Single 9854 23352 3 Y
## 632 Single 9888 6770 1 Y
## 384 Divorced 2322 9518 3 Y
## 644 Married 2307 14460 1 Y
## 792 Single 2311 5711 2 Y
## 525 Married 2177 8456 3 Y
## 677 Married 5228 23361 0 Y
## 807 Married 6232 12477 2 Y
## 825 Married 4663 12421 9 Y
## 551 Married 19189 19562 1 Y
## 328 Divorced 5410 11189 6 Y
## 356 Single 4759 15891 3 Y
## 77 Married 7083 12288 1 Y
## 116 Single 7642 4814 1 Y
## 450 Married 4385 24162 1 Y
## 701 Married 2804 15434 1 Y
## 443 Single 3485 14935 2 Y
## 739 Married 4601 6179 1 Y
## 835 Divorced 13577 25592 1 Y
## 160 Married 13191 23281 3 Y
## 850 Married 2544 7102 0 Y
## 604 Married 3131 26342 1 Y
## 165 Married 8020 5100 0 Y
## 606 Married 14814 13514 3 Y
## 524 Divorced 19502 2125 1 Y
## 126 Married 5204 13586 1 Y
## 93 Single 2090 2396 6 Y
## 418 Married 3688 7122 4 Y
## 64 Single 1514 8018 1 Y
## 685 Married 2713 6672 1 Y
## 293 Single 2279 11781 1 Y
## 727 Married 2973 21222 5 Y
## 729 Single 2340 23213 1 Y
## 860 Married 7978 14075 1 Y
## 609 Single 3578 23577 0 Y
## 246 Divorced 2532 6054 6 Y
## 375 Single 2728 21082 1 Y
## 159 Married 10820 11535 8 Y
## 108 Married 19636 25811 4 Y
## 489 Single 5381 19294 9 Y
## 391 Married 6500 13305 5 Y
## 82 Married 6883 5151 2 Y
## 379 Married 2168 26933 0 Y
## 439 Married 2683 3810 1 Y
## 681 Married 4256 18154 1 Y
## 477 Divorced 6334 24558 4 Y
## 314 Married 6132 13983 2 Y
## 843 Married 13826 19028 3 Y
## 863 Single 5304 4652 8 Y
## 839 Divorced 10333 19271 8 Y
## 362 Single 1091 10642 1 Y
## 660 Single 6499 22656 1 Y
## 643 Single 16756 17323 7 Y
## 310 Married 4851 15678 0 Y
## 45 Married 1223 16901 1 Y
## 390 Single 2174 9150 1 Y
## 193 Divorced 4810 26314 2 Y
## 494 Single 8606 21195 1 Y
## 34 Married 2258 16340 6 Y
## 78 Single 17567 3156 1 Y
## 344 Divorced 2083 22653 1 Y
## 753 Married 11245 20689 2 Y
## 202 Married 6220 7346 1 Y
## 6 Divorced 8793 4809 1 Y
## 36 Married 2794 26062 1 Y
## 849 Married 6380 6110 2 Y
## 340 Divorced 2341 19715 1 Y
## 132 Single 4507 8191 3 Y
## 373 Divorced 5210 20308 1 Y
## 747 Divorced 2335 3157 4 Y
## 44 Single 19613 26362 8 Y
## 267 Divorced 17007 11929 7 Y
## 410 Married 17159 5200 6 Y
## 760 Single 18824 2493 2 Y
## 788 Married 4941 17747 2 Y
## 596 Divorced 4037 21816 1 Y
## 302 Married 9069 11031 1 Y
## 731 Divorced 4765 23814 4 Y
## 56 Divorced 2289 20520 1 Y
## 454 Single 5487 10410 1 Y
## 382 Married 2942 8916 1 Y
## 647 Single 1904 13556 1 Y
## 445 Divorced 19328 14218 7 Y
## 804 Single 9582 10333 0 Y
## 104 Married 2819 8544 2 Y
## 437 Single 4382 16374 6 Y
## 656 Married 4741 22722 1 Y
## 679 Divorced 5131 9192 7 Y
## 752 Single 2302 8319 1 Y
## 426 Divorced 10798 5268 5 Y
## 296 Married 2660 20232 7 Y
## 420 Married 11935 21526 1 Y
## 830 Married 18200 7999 1 Y
## 32 Married 2028 13637 1 Y
## 726 Married 4898 7505 0 Y
## 166 Single 2044 22052 1 Y
## 247 Single 7143 25713 1 Y
## 117 Single 4950 20623 0 Y
## 845 Single 6029 25353 5 Y
## 364 Divorced 19436 5949 0 Y
## 557 Single 3780 23428 7 Y
## 376 Single 14411 24450 1 Y
## 98 Divorced 5163 15850 5 Y
## 502 Single 2028 12947 5 Y
## 736 Single 6500 26997 0 Y
## 180 Single 8686 12930 4 Y
## 338 Single 7756 22266 0 Y
## 501 Married 10008 12023 7 Y
## 778 Divorced 2827 14947 1 Y
## 531 Married 8823 24608 0 Y
## 744 Single 3295 12862 1 Y
## 196 Married 2723 23231 1 Y
## 30 Married 4258 26589 0 Y
## 31 Divorced 4668 22812 0 Y
## 400 Single 10209 19719 5 Y
## 95 Single 19926 17053 3 Y
## 49 Single 1878 8059 1 Y
## 771 Single 4682 4317 3 Y
## 659 Single 6032 10110 6 Y
## 465 Married 2437 15587 9 Y
## 698 Single 5914 9945 8 Y
## 798 Married 2398 15999 1 Y
## 782 Single 4553 20978 1 Y
## 291 Divorced 2270 11005 3 Y
## 764 Married 6347 24920 0 Y
## 211 Married 13570 5640 0 Y
## 634 Divorced 2835 2561 5 Y
## 654 Married 12504 23978 3 Y
## 775 Married 10124 18611 2 Y
## 559 Married 4434 11806 1 Y
## 805 Married 2042 25043 4 Y
## 178 Married 2042 15346 6 Y
## 110 Single 2439 11288 1 Y
## 857 Single 2657 8556 5 Y
## 337 Divorced 4968 18500 1 Y
## 115 Married 8966 21026 3 Y
## 585 Married 6465 19121 2 Y
## 460 Single 5810 22604 1 Y
## 505 Married 17169 26703 3 Y
## 576 Divorced 5993 2689 1 Y
## 799 Single 1859 6148 1 Y
## 856 Married 2404 16192 1 Y
## 233 Married 12936 24164 7 Y
## 780 Married 4787 26124 9 Y
## 129 Single 4081 20003 1 Y
## 837 Married 2996 5182 7 Y
## 766 Single 5769 23447 1 Y
## 569 Single 6244 7824 7 Y
## 388 Divorced 4262 22645 4 Y
## 321 Married 2332 3974 6 Y
## 308 Married 2979 22478 3 Y
## 829 Married 2468 15963 4 Y
## 94 Single 6142 4223 3 Y
## 289 Married 9738 22952 0 Y
## 408 Divorced 2741 6865 1 Y
## 368 Married 5473 19345 0 Y
## 164 Single 6545 23016 3 Y
## 413 Married 3022 10227 4 Y
## 487 Single 1081 16019 1 Y
## 767 Married 3376 18863 1 Y
## 733 Married 2863 19555 1 Y
## 735 Single 2972 22061 1 Y
## 808 Married 2323 11992 1 Y
## 458 Single 5441 8423 0 Y
## 552 Divorced 4936 23965 1 Y
## 53 Married 2011 19982 1 Y
## 409 Married 15427 22021 2 Y
## 702 Married 5714 5829 1 Y
## 280 Married 4035 16143 0 Y
## 327 Divorced 19049 3549 0 Y
## 415 Divorced 2351 12253 0 Y
## 83 Married 5321 14284 2 Y
## 396 Divorced 2695 7747 0 Y
## 133 Married 4523 4386 0 Y
## 275 Single 18722 13339 8 Y
## 456 Single 19833 4349 1 Y
## 518 Divorced 2766 8952 8 Y
## 304 Single 5098 18698 1 Y
## 238 Married 6322 18089 1 Y
## 507 Single 2851 9150 1 Y
## 482 Married 2356 14871 3 Y
## 476 Single 5772 20445 4 Y
## 434 Divorced 2109 10007 1 Y
## 495 Married 5673 6060 1 Y
## 724 Single 8858 15669 0 Y
## 47 Single 6735 12147 6 Y
## 147 Married 2756 4673 1 Y
## 331 Single 15379 22384 4 Y
## 218 Divorced 17174 2437 3 Y
## 555 Married 4876 14242 9 Y
## 201 Single 13194 17071 4 Y
## 587 Divorced 5329 15717 7 Y
## 290 Married 2572 20317 1 Y
## 467 Single 2644 17001 3 Y
## 393 Divorced 3065 3995 1 Y
## 255 Divorced 13757 25178 2 Y
## 558 Married 3622 22794 1 Y
## 300 Married 3464 24737 5 Y
## 253 Single 2274 6153 1 Y
## 592 Single 4599 7815 0 Y
## 59 Divorced 13603 11677 2 Y
## 811 Divorced 3072 11012 1 Y
## 278 Single 2455 10675 0 Y
## 137 Married 2319 6689 1 Y
## 225 Divorced 4771 14293 2 Y
## 191 Married 5775 11934 1 Y
## 544 Married 6553 7259 9 Y
## 473 Married 13245 15067 4 Y
## 523 Divorced 9724 18787 2 Y
## 710 Divorced 17924 4544 1 Y
## 861 Single 2838 4257 0 Y
## 182 Divorced 6893 19461 3 Y
## 852 Single 2206 16117 1 Y
## 236 Married 10609 14922 5 Y
## 779 Single 2570 11925 1 Y
## 187 Divorced 5206 4973 1 Y
## 717 Married 6578 2706 1 Y
## 740 Single 3681 14004 4 Y
## 629 Married 10976 15813 3 Y
## 311 Married 5484 13008 9 Y
## 399 Divorced 6306 26236 1 Y
## 131 Divorced 4187 3356 1 Y
## 486 Single 5993 19479 8 Y
## 370 Single 5055 10557 7 Y
## 199 Married 10248 2094 3 Y
## 783 Divorced 2277 22650 3 Y
## 566 Single 2436 13422 6 Y
## 226 Married 3748 4077 1 Y
## 488 Divorced 2911 15170 1 Y
## 614 Single 2121 9947 1 Y
## 320 Single 2994 21221 1 Y
## 800 Divorced 6842 26308 6 Y
## 700 Married 19081 10849 5 Y
## 762 Married 16752 12982 1 Y
## 684 Divorced 9439 23402 3 Y
## 220 Single 2875 9973 1 Y
## 229 Married 4999 17519 0 Y
## 11 Married 19392 22539 7 Y
## 342 Married 3867 14222 1 Y
## 716 Married 1555 11585 1 Y
## 65 Married 2210 3372 1 Y
## 259 Married 9713 24444 2 Y
## 540 Single 5155 2253 7 Y
## 24 Single 5332 21602 7 Y
## 134 Married 5295 7693 4 Y
## 590 Married 4014 16002 3 Y
## 286 Married 5207 22949 1 Y
## 12 Divorced 2706 10494 1 Y
## 249 Divorced 5467 13953 3 Y
## 529 Single 2859 26278 1 Y
## 215 Single 2045 15174 0 Y
## 449 Single 2406 5456 1 Y
## 20 Married 5033 9364 2 Y
## 530 Married 4963 4510 9 Y
## 294 Married 11957 17231 0 Y
## 89 Single 8726 2975 1 Y
## 435 Divorced 9705 20652 2 Y
## 436 Married 6214 3415 1 Y
## 330 Married 16799 16616 0 Y
## 639 Married 2725 21630 1 Y
## 663 Divorced 10368 5596 4 Y
## 469 Single 4969 21813 8 Y
## 868 Married 4591 24200 3 Y
## 713 Single 2559 16620 5 Y
## 594 Married 3211 22102 1 Y
## 579 Single 5042 3140 0 Y
## 646 Married 10855 8552 7 Y
## 633 Single 6861 4981 8 Y
## 581 Married 3669 9075 3 Y
## 513 Married 3433 17360 6 Y
## 619 Married 9991 21457 4 Y
## 282 Single 4721 3119 2 Y
## 791 Married 4789 23070 4 Y
## 699 Single 1569 18420 1 Y
## 601 Married 2033 7103 1 Y
## 210 Divorced 8189 21196 3 Y
## 172 Married 5467 2125 8 Y
## 22 Married 8120 18597 3 Y
## 453 Single 5813 13492 1 Y
## 776 Single 3505 19630 1 Y
## 626 Divorced 2517 3208 1 Y
## 252 Divorced 5079 25952 4 Y
## 480 Married 13744 15471 1 Y
## 648 Married 2305 6217 1 Y
## 840 Married 19665 13583 4 Y
## 814 Married 19658 5220 3 Y
## 285 Married 2844 6004 1 Y
## 438 Single 2867 20006 0 Y
## 279 Single 5744 26959 1 Y
## 203 Married 6162 19124 1 Y
## 405 Single 2559 17852 1 Y
## 138 Married 10739 13943 8 Y
## 217 Married 9824 22174 3 Y
## 854 Single 5661 4824 0 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 834 No 25 4 3
## 697 No 25 4 2
## 207 No 13 3 2
## 715 No 15 3 2
## 809 No 12 3 2
## 725 No 13 3 4
## 222 No 14 3 4
## 71 No 22 4 1
## 503 Yes 11 3 3
## 403 No 12 3 3
## 387 No 14 3 1
## 806 Yes 13 3 4
## 826 No 17 3 4
## 821 No 17 3 4
## 844 No 11 3 4
## 528 Yes 12 3 4
## 128 No 13 3 4
## 539 Yes 16 3 2
## 457 No 11 3 1
## 732 Yes 18 3 1
## 842 No 13 3 1
## 85 Yes 11 3 4
## 122 Yes 11 3 4
## 474 No 13 3 2
## 819 No 13 3 4
## 625 No 11 3 3
## 184 No 18 3 4
## 92 No 13 3 1
## 269 No 20 4 3
## 463 No 11 3 4
## 407 No 14 3 1
## 235 Yes 12 3 4
## 54 No 12 3 2
## 146 No 14 3 1
## 464 No 20 4 4
## 192 No 14 3 4
## 665 No 20 4 3
## 394 No 19 3 4
## 810 Yes 16 3 4
## 512 No 21 4 2
## 628 Yes 14 3 2
## 248 No 14 3 2
## 8 Yes 14 3 3
## 468 No 12 3 2
## 851 Yes 11 3 3
## 404 No 12 3 3
## 365 No 14 3 4
## 491 Yes 11 3 4
## 616 No 14 3 3
## 748 No 20 4 3
## 123 Yes 12 3 3
## 841 No 13 3 3
## 326 No 19 3 3
## 149 No 22 4 4
## 91 Yes 23 4 3
## 72 No 18 3 4
## 563 No 14 3 2
## 80 Yes 11 3 3
## 828 Yes 12 3 4
## 481 No 12 3 1
## 466 Yes 11 3 3
## 510 No 12 3 4
## 381 No 11 3 2
## 90 No 12 3 4
## 127 Yes 22 4 2
## 737 No 11 3 1
## 527 Yes 11 3 1
## 100 No 17 3 1
## 209 No 14 3 4
## 148 No 13 3 4
## 125 No 18 3 1
## 754 No 21 4 3
## 139 Yes 21 4 4
## 649 No 14 3 2
## 516 No 18 3 4
## 3 No 11 3 3
## 412 No 12 3 2
## 667 No 14 3 4
## 227 No 12 3 3
## 4 No 19 3 3
## 27 No 11 3 4
## 240 No 15 3 3
## 169 Yes 12 3 2
## 422 Yes 15 3 1
## 130 No 12 3 4
## 784 No 12 3 1
## 188 No 14 3 3
## 521 No 16 3 3
## 745 Yes 22 4 2
## 256 No 13 3 3
## 145 No 20 4 2
## 262 No 13 3 3
## 369 No 16 3 4
## 573 Yes 12 3 3
## 135 No 11 3 2
## 152 No 12 3 4
## 374 No 11 3 1
## 822 No 14 3 4
## 785 No 11 3 4
## 446 No 12 3 3
## 793 Yes 19 3 4
## 17 No 12 3 3
## 718 No 13 3 4
## 176 Yes 11 3 3
## 319 Yes 12 3 4
## 372 No 11 3 3
## 461 No 17 3 4
## 361 No 14 3 1
## 141 Yes 12 3 1
## 28 Yes 17 3 1
## 423 Yes 18 3 2
## 363 No 15 3 4
## 796 Yes 11 3 1
## 859 No 21 4 4
## 295 Yes 21 4 4
## 574 No 21 4 3
## 703 Yes 11 3 2
## 334 No 11 3 4
## 268 No 12 3 1
## 272 No 12 3 3
## 671 No 13 3 1
## 51 No 17 3 4
## 153 No 14 3 3
## 441 Yes 14 3 4
## 154 No 12 3 1
## 87 No 12 3 4
## 575 No 20 4 4
## 414 No 13 3 3
## 763 No 13 3 1
## 58 Yes 18 3 4
## 224 No 14 3 4
## 580 Yes 11 3 2
## 622 Yes 19 3 3
## 155 No 14 3 2
## 847 No 14 3 4
## 542 No 19 3 2
## 395 Yes 14 3 4
## 67 No 13 3 2
## 25 No 14 3 1
## 288 No 11 3 3
## 358 No 16 3 2
## 827 No 13 3 1
## 728 Yes 17 3 4
## 730 No 14 3 3
## 303 No 12 3 4
## 69 No 13 3 4
## 264 No 15 3 2
## 562 Yes 14 3 2
## 675 No 17 3 1
## 427 No 13 3 2
## 223 Yes 25 4 3
## 35 No 24 4 2
## 533 No 24 4 3
## 239 No 14 3 3
## 243 No 23 4 4
## 707 No 15 3 2
## 43 Yes 16 3 2
## 777 No 21 4 1
## 419 No 13 3 2
## 333 Yes 12 3 3
## 640 No 11 3 2
## 757 Yes 25 4 4
## 774 Yes 17 3 1
## 864 Yes 12 3 4
## 499 No 13 3 2
## 484 Yes 18 3 3
## 281 Yes 17 3 1
## 84 Yes 13 3 1
## 761 Yes 20 4 3
## 37 No 17 3 3
## 292 No 15 3 3
## 537 No 16 3 3
## 118 Yes 13 3 1
## 261 No 14 3 3
## 213 No 17 3 1
## 459 No 22 4 4
## 593 No 15 3 4
## 448 No 18 3 3
## 26 No 11 3 3
## 599 Yes 20 4 2
## 113 No 19 3 4
## 345 No 19 3 3
## 870 No 11 3 4
## 177 Yes 13 3 4
## 156 Yes 12 3 2
## 568 No 11 3 4
## 673 No 14 3 4
## 432 No 16 3 2
## 759 No 19 3 4
## 440 No 13 3 3
## 347 No 11 3 4
## 833 No 12 3 3
## 274 No 13 3 1
## 40 No 12 3 1
## 636 No 13 3 2
## 2 No 14 3 1
## 216 No 14 3 3
## 312 No 15 3 3
## 305 No 24 4 1
## 838 No 12 3 4
## 686 No 25 4 4
## 162 No 12 3 1
## 866 No 11 3 3
## 832 Yes 19 3 4
## 627 Yes 12 3 3
## 329 Yes 16 3 3
## 179 No 14 3 4
## 545 No 11 3 3
## 397 No 13 3 3
## 185 No 11 3 1
## 357 No 19 3 3
## 170 No 22 4 1
## 251 No 12 3 3
## 638 No 18 3 3
## 869 Yes 19 3 3
## 429 Yes 14 3 4
## 57 Yes 12 3 4
## 705 No 17 3 4
## 55 No 23 4 4
## 853 Yes 12 3 4
## 478 No 12 3 1
## 797 Yes 14 3 2
## 770 No 13 3 4
## 257 No 11 3 3
## 79 No 12 3 4
## 66 Yes 12 3 4
## 687 Yes 12 3 1
## 339 Yes 14 3 4
## 631 No 20 4 2
## 676 No 14 3 3
## 143 No 13 3 2
## 561 No 12 3 3
## 254 No 17 3 3
## 189 No 14 3 1
## 1 No 11 3 3
## 858 No 24 4 1
## 578 No 14 3 4
## 234 No 14 3 4
## 751 Yes 14 3 2
## 299 Yes 22 4 1
## 543 Yes 15 3 4
## 214 Yes 13 3 4
## 366 No 11 3 4
## 624 Yes 16 3 3
## 610 No 19 3 4
## 250 No 16 3 2
## 119 No 12 3 1
## 103 No 15 3 1
## 221 No 22 4 3
## 630 No 14 3 2
## 605 No 13 3 3
## 462 No 11 3 3
## 714 No 11 3 1
## 517 Yes 18 3 3
## 287 Yes 23 4 1
## 519 No 12 3 1
## 674 Yes 19 3 1
## 392 Yes 19 3 3
## 758 No 14 3 2
## 514 Yes 12 3 3
## 668 No 13 3 1
## 317 No 18 3 4
## 52 Yes 21 4 3
## 511 No 11 3 3
## 815 No 19 3 4
## 341 Yes 14 3 4
## 612 Yes 17 3 1
## 564 No 20 4 3
## 183 No 12 3 4
## 97 No 12 3 4
## 602 No 14 3 2
## 711 Yes 23 4 4
## 586 Yes 15 3 3
## 642 No 12 3 2
## 651 No 19 3 2
## 666 Yes 12 3 4
## 508 No 11 3 2
## 70 No 12 3 2
## 73 Yes 13 3 1
## 509 No 17 3 3
## 704 Yes 11 3 3
## 455 No 11 3 1
## 662 No 12 3 3
## 7 Yes 12 3 1
## 198 No 17 3 2
## 442 No 17 3 3
## 756 No 14 3 3
## 322 No 12 3 4
## 265 No 12 3 3
## 150 No 21 4 2
## 232 No 13 3 1
## 421 No 15 3 3
## 231 No 19 3 1
## 786 No 11 3 3
## 241 Yes 21 4 4
## 228 Yes 16 3 1
## 284 No 12 3 4
## 99 No 13 3 3
## 181 No 18 3 2
## 496 No 14 3 3
## 39 No 14 3 1
## 212 No 13 3 2
## 107 No 13 3 1
## 325 No 16 3 3
## 635 No 16 3 1
## 343 No 15 3 3
## 237 No 11 3 1
## 206 Yes 13 3 4
## 607 No 11 3 4
## 318 No 12 3 2
## 772 No 25 4 3
## 645 No 17 3 1
## 749 No 13 3 2
## 385 Yes 12 3 3
## 794 No 13 3 2
## 313 No 12 3 1
## 380 Yes 11 3 2
## 260 Yes 19 3 3
## 10 No 14 3 2
## 447 No 15 3 4
## 534 No 18 3 2
## 497 No 21 4 4
## 352 No 11 3 2
## 746 No 14 3 2
## 158 No 11 3 4
## 483 Yes 15 3 2
## 323 No 12 3 2
## 324 Yes 23 4 1
## 535 Yes 11 3 4
## 307 No 18 3 2
## 694 Yes 23 4 3
## 750 No 14 3 1
## 16 No 13 3 4
## 855 Yes 11 3 4
## 823 No 21 4 4
## 591 Yes 14 3 2
## 360 No 11 3 3
## 470 No 11 3 3
## 68 No 13 3 3
## 548 No 14 3 4
## 670 No 14 3 1
## 424 No 15 3 3
## 121 No 14 3 1
## 818 No 11 3 4
## 258 No 14 3 2
## 696 Yes 18 3 1
## 812 No 11 3 2
## 479 No 13 3 3
## 490 No 11 3 4
## 867 No 18 3 1
## 471 No 11 3 3
## 862 No 14 3 4
## 824 No 12 3 3
## 522 Yes 17 3 3
## 401 No 20 4 3
## 353 Yes 13 3 4
## 163 No 12 3 4
## 492 Yes 11 3 2
## 820 Yes 11 3 4
## 632 No 21 4 1
## 384 No 13 3 3
## 644 Yes 23 4 2
## 792 No 15 3 4
## 525 No 17 3 1
## 677 No 15 3 1
## 807 No 11 3 2
## 825 Yes 12 3 2
## 551 No 12 3 2
## 328 Yes 17 3 3
## 356 No 18 3 4
## 77 Yes 14 3 4
## 116 Yes 13 3 4
## 450 No 15 3 1
## 701 No 11 3 4
## 443 No 11 3 3
## 739 No 16 3 2
## 835 Yes 15 3 4
## 160 Yes 17 3 3
## 850 No 18 3 1
## 604 No 13 3 1
## 165 No 15 3 3
## 606 No 19 3 3
## 524 Yes 17 3 3
## 126 Yes 11 3 4
## 93 Yes 15 3 2
## 418 No 18 3 4
## 64 No 16 3 3
## 685 No 11 3 3
## 293 No 16 3 4
## 727 No 15 3 2
## 729 Yes 18 3 2
## 860 No 11 3 4
## 609 No 12 3 4
## 246 No 14 3 3
## 375 No 11 3 1
## 159 No 11 3 3
## 108 Yes 18 3 1
## 489 Yes 13 3 4
## 391 No 17 3 2
## 82 No 16 3 2
## 379 Yes 18 3 1
## 439 Yes 14 3 3
## 681 No 12 3 1
## 477 No 19 3 4
## 314 No 17 3 3
## 843 No 22 4 3
## 863 Yes 13 3 2
## 839 Yes 12 3 3
## 362 No 17 3 4
## 660 No 13 3 3
## 643 No 15 3 2
## 310 No 22 4 3
## 45 No 22 4 4
## 390 Yes 11 3 3
## 193 No 14 3 3
## 494 No 19 3 4
## 34 No 12 3 2
## 78 No 15 3 2
## 344 No 20 4 3
## 753 Yes 15 3 3
## 202 No 17 3 2
## 6 No 21 4 3
## 36 No 20 4 3
## 849 Yes 12 3 1
## 340 No 13 3 4
## 132 No 12 3 3
## 373 No 18 3 1
## 747 Yes 15 3 4
## 44 No 22 4 4
## 267 No 14 3 4
## 410 No 24 4 3
## 760 Yes 16 3 1
## 788 No 15 3 1
## 596 No 22 4 1
## 302 No 22 4 4
## 731 No 21 4 3
## 56 No 20 4 2
## 454 No 14 3 2
## 382 No 23 4 4
## 647 No 12 3 4
## 445 Yes 17 3 3
## 804 Yes 22 4 1
## 104 No 16 3 1
## 437 No 17 3 4
## 656 Yes 13 3 3
## 679 No 13 3 2
## 752 Yes 11 3 1
## 426 No 13 3 3
## 296 Yes 11 3 3
## 420 No 18 3 3
## 830 No 11 3 3
## 32 No 18 3 4
## 726 No 12 3 4
## 166 No 13 3 4
## 247 Yes 14 3 3
## 117 No 14 3 2
## 845 No 12 3 1
## 364 No 19 3 4
## 557 No 11 3 3
## 376 Yes 13 3 4
## 98 No 14 3 4
## 502 Yes 14 3 2
## 736 No 14 3 2
## 180 No 22 4 3
## 338 No 17 3 3
## 501 Yes 14 3 4
## 778 No 12 3 3
## 531 No 18 3 1
## 744 No 13 3 3
## 196 No 11 3 2
## 30 No 18 3 1
## 31 No 17 3 4
## 400 Yes 18 3 2
## 95 No 15 3 2
## 49 Yes 14 3 4
## 771 No 14 3 3
## 659 Yes 15 3 4
## 465 Yes 16 3 4
## 698 No 16 3 4
## 798 Yes 17 3 3
## 782 No 11 3 1
## 291 No 14 3 4
## 764 No 12 3 1
## 211 No 23 4 3
## 634 No 22 4 1
## 654 No 21 4 2
## 775 Yes 14 3 3
## 559 No 13 3 4
## 805 No 12 3 3
## 178 Yes 14 3 2
## 110 No 14 3 4
## 857 Yes 11 3 3
## 337 No 11 3 4
## 115 Yes 15 3 4
## 585 Yes 13 3 4
## 460 No 16 3 3
## 505 No 19 3 2
## 576 No 18 3 3
## 799 Yes 25 4 2
## 856 No 13 3 1
## 233 No 11 3 3
## 780 Yes 14 3 2
## 129 Yes 14 3 1
## 837 Yes 15 3 4
## 766 Yes 14 3 1
## 569 No 17 3 1
## 388 No 12 3 2
## 321 No 20 4 3
## 308 No 17 3 4
## 829 No 14 3 2
## 94 Yes 16 3 3
## 289 No 14 3 3
## 408 No 14 3 3
## 368 No 12 3 4
## 164 Yes 13 3 3
## 413 No 21 4 1
## 487 No 13 3 3
## 767 No 13 3 3
## 733 No 12 3 1
## 735 No 13 3 3
## 808 No 24 4 1
## 458 Yes 22 4 4
## 552 No 13 3 4
## 53 No 13 3 4
## 409 No 16 3 3
## 702 No 20 4 1
## 280 Yes 16 3 2
## 327 Yes 14 3 4
## 415 No 16 3 4
## 83 No 11 3 4
## 396 Yes 18 3 2
## 133 No 11 3 4
## 275 No 11 3 4
## 456 No 14 3 2
## 518 No 22 4 2
## 304 No 19 3 2
## 238 Yes 12 3 4
## 507 Yes 13 3 2
## 482 Yes 19 3 2
## 476 Yes 21 4 3
## 434 No 13 3 3
## 495 Yes 13 3 1
## 724 No 11 3 2
## 47 No 15 3 2
## 147 No 13 3 4
## 331 No 14 3 1
## 218 No 11 3 2
## 555 No 14 3 4
## 201 Yes 16 3 4
## 587 Yes 12 3 4
## 290 No 16 3 2
## 467 Yes 21 4 4
## 393 Yes 13 3 4
## 255 No 11 3 3
## 558 Yes 13 3 4
## 300 Yes 13 3 4
## 253 No 14 3 4
## 592 Yes 23 4 3
## 59 Yes 18 3 1
## 811 No 11 3 3
## 278 No 19 3 1
## 137 Yes 11 3 4
## 225 No 19 3 4
## 191 No 13 3 4
## 544 No 14 3 2
## 473 Yes 14 3 2
## 523 No 17 3 3
## 710 No 11 3 4
## 861 No 14 3 2
## 182 No 15 3 4
## 852 No 13 3 1
## 236 No 11 3 3
## 779 No 20 4 3
## 187 No 17 3 3
## 717 No 18 3 1
## 740 No 14 3 4
## 629 No 18 3 2
## 311 No 17 3 2
## 399 No 21 4 1
## 131 Yes 13 3 2
## 486 Yes 11 3 1
## 370 No 16 3 3
## 199 No 14 3 4
## 783 Yes 11 3 3
## 566 Yes 12 3 3
## 226 No 13 3 3
## 488 No 17 3 4
## 614 Yes 13 3 2
## 320 Yes 12 3 4
## 800 No 20 4 1
## 700 No 11 3 1
## 762 Yes 11 3 3
## 684 Yes 16 3 2
## 220 Yes 20 4 2
## 229 No 21 4 1
## 11 No 13 3 4
## 342 Yes 12 3 2
## 716 No 11 3 3
## 65 No 13 3 1
## 259 Yes 13 3 4
## 540 No 13 3 4
## 24 No 13 3 4
## 134 No 21 4 3
## 590 Yes 15 3 3
## 286 Yes 12 3 2
## 12 No 15 3 2
## 249 Yes 14 3 1
## 529 No 18 3 1
## 215 No 13 3 4
## 449 No 11 3 4
## 20 No 15 3 4
## 530 Yes 18 3 1
## 294 No 18 3 1
## 89 No 15 3 4
## 435 No 12 3 2
## 436 No 18 3 1
## 330 No 14 3 3
## 639 Yes 11 3 2
## 663 Yes 12 3 2
## 469 No 18 3 4
## 868 Yes 17 3 3
## 713 No 11 3 3
## 594 No 14 3 4
## 579 No 13 3 4
## 646 No 11 3 1
## 633 Yes 12 3 3
## 581 No 11 3 3
## 513 No 13 3 1
## 619 No 15 3 1
## 282 Yes 13 3 3
## 791 No 25 4 1
## 699 Yes 12 3 3
## 601 No 13 3 3
## 210 Yes 13 3 3
## 172 No 18 3 3
## 22 No 12 3 4
## 453 Yes 18 3 4
## 776 No 18 3 4
## 626 No 11 3 2
## 252 No 13 3 4
## 480 Yes 25 4 1
## 648 No 15 3 3
## 840 No 12 3 4
## 814 No 11 3 3
## 285 No 13 3 4
## 438 No 13 3 4
## 279 Yes 11 3 4
## 203 No 12 3 3
## 405 No 11 3 4
## 138 No 11 3 3
## 217 No 19 3 3
## 854 No 19 3 3
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 834 80 0 1 4
## 697 80 1 23 3
## 207 80 0 7 2
## 715 80 0 10 1
## 809 80 1 10 2
## 725 80 0 8 3
## 222 80 2 5 4
## 71 80 1 12 3
## 503 80 2 19 3
## 403 80 0 7 2
## 387 80 0 10 3
## 806 80 1 15 1
## 826 80 2 23 0
## 821 80 2 6 3
## 844 80 1 10 0
## 528 80 0 6 2
## 128 80 2 10 2
## 539 80 0 6 4
## 457 80 2 10 2
## 732 80 0 12 3
## 842 80 0 10 3
## 85 80 0 18 2
## 122 80 0 2 6
## 474 80 0 9 2
## 819 80 0 6 3
## 625 80 0 8 2
## 184 80 0 9 3
## 92 80 1 11 3
## 269 80 3 9 2
## 463 80 0 16 3
## 407 80 2 13 3
## 235 80 2 25 3
## 54 80 0 9 5
## 146 80 1 4 3
## 464 80 0 9 3
## 192 80 2 9 3
## 665 80 0 10 2
## 394 80 0 22 5
## 810 80 1 6 3
## 512 80 1 13 2
## 628 80 1 12 3
## 248 80 1 3 2
## 8 80 3 8 5
## 468 80 0 5 3
## 851 80 1 4 2
## 404 80 2 9 6
## 365 80 1 23 5
## 491 80 0 22 4
## 616 80 1 15 3
## 748 80 0 14 3
## 123 80 1 18 2
## 841 80 0 0 2
## 326 80 0 1 2
## 149 80 0 6 2
## 91 80 1 1 3
## 72 80 0 6 4
## 563 80 1 4 2
## 80 80 3 1 4
## 828 80 0 4 3
## 481 80 1 6 5
## 466 80 0 6 3
## 510 80 0 23 0
## 381 80 1 21 2
## 90 80 0 22 2
## 127 80 0 10 5
## 737 80 1 21 2
## 527 80 1 9 3
## 100 80 1 7 2
## 209 80 1 8 3
## 148 80 1 8 6
## 125 80 0 10 3
## 754 80 3 14 2
## 139 80 0 8 2
## 649 80 0 16 2
## 516 80 1 13 3
## 3 80 0 10 2
## 412 80 0 10 3
## 667 80 1 11 5
## 227 80 1 1 3
## 4 80 2 14 3
## 27 80 1 10 3
## 240 80 3 19 3
## 169 80 2 18 3
## 422 80 2 15 2
## 130 80 1 25 5
## 784 80 1 10 3
## 188 80 3 29 2
## 521 80 0 6 3
## 745 80 1 6 3
## 256 80 0 7 2
## 145 80 3 15 0
## 262 80 0 11 2
## 369 80 1 4 2
## 573 80 1 7 2
## 135 80 1 9 3
## 152 80 0 10 2
## 374 80 1 8 2
## 822 80 1 10 2
## 785 80 1 4 3
## 446 80 1 17 2
## 793 80 1 10 6
## 17 80 1 8 2
## 718 80 0 10 4
## 176 80 0 2 6
## 319 80 2 8 2
## 372 80 0 10 3
## 461 80 1 8 4
## 361 80 1 34 5
## 141 80 1 33 0
## 28 80 0 6 3
## 423 80 0 12 3
## 363 80 1 33 3
## 796 80 0 10 3
## 859 80 1 7 2
## 295 80 0 21 2
## 574 80 2 10 5
## 703 80 1 16 6
## 334 80 0 9 3
## 268 80 0 1 2
## 272 80 1 11 2
## 671 80 0 16 3
## 51 80 0 8 3
## 153 80 1 13 3
## 441 80 0 10 2
## 154 80 0 11 3
## 87 80 0 20 2
## 575 80 2 6 5
## 414 80 0 4 2
## 763 80 0 19 4
## 58 80 0 10 3
## 224 80 1 1 2
## 580 80 2 6 2
## 622 80 0 20 3
## 155 80 2 1 3
## 847 80 0 1 3
## 542 80 1 14 1
## 395 80 0 8 2
## 67 80 0 17 3
## 25 80 0 6 3
## 288 80 1 3 2
## 358 80 0 6 2
## 827 80 3 10 2
## 728 80 1 5 4
## 730 80 1 3 2
## 303 80 1 8 1
## 69 80 1 5 2
## 264 80 1 3 5
## 562 80 0 23 3
## 675 80 0 5 0
## 427 80 2 1 2
## 223 80 1 7 2
## 35 80 0 8 1
## 533 80 1 10 3
## 239 80 0 6 3
## 243 80 1 6 3
## 707 80 1 14 2
## 43 80 1 17 2
## 777 80 1 6 3
## 419 80 1 14 2
## 333 80 3 6 2
## 640 80 1 5 3
## 757 80 1 10 2
## 774 80 1 2 0
## 864 80 2 28 2
## 499 80 2 17 3
## 484 80 1 19 3
## 281 80 1 10 3
## 84 80 2 13 2
## 761 80 0 3 5
## 37 80 0 15 3
## 292 80 1 11 3
## 537 80 0 10 2
## 118 80 1 10 2
## 261 80 0 3 3
## 213 80 0 4 2
## 459 80 1 5 3
## 593 80 1 5 3
## 448 80 2 10 2
## 26 80 1 26 5
## 599 80 1 6 0
## 113 80 0 18 4
## 345 80 3 8 3
## 870 80 0 10 5
## 177 80 1 24 2
## 156 80 1 4 2
## 568 80 1 10 4
## 673 80 0 5 5
## 432 80 1 6 0
## 759 80 1 7 5
## 440 80 0 10 4
## 347 80 0 5 3
## 833 80 3 6 2
## 274 80 2 11 3
## 40 80 1 3 4
## 636 80 1 2 2
## 2 80 0 21 2
## 216 80 1 8 3
## 312 80 0 22 3
## 305 80 0 7 4
## 838 80 0 10 3
## 686 80 1 13 6
## 162 80 0 10 2
## 866 80 0 11 2
## 832 80 0 0 2
## 627 80 2 5 3
## 329 80 1 20 3
## 179 80 0 9 2
## 545 80 0 6 3
## 397 80 0 26 2
## 185 80 1 8 3
## 357 80 0 5 3
## 170 80 1 11 1
## 251 80 3 1 2
## 638 80 1 9 2
## 869 80 1 7 2
## 429 80 0 4 2
## 57 80 0 7 2
## 705 80 0 7 2
## 55 80 0 11 2
## 853 80 0 10 3
## 478 80 2 8 5
## 797 80 0 21 2
## 770 80 1 15 2
## 257 80 1 2 5
## 79 80 0 23 2
## 66 80 2 7 5
## 687 80 0 9 1
## 339 80 0 5 4
## 631 80 1 19 2
## 676 80 1 26 2
## 143 80 1 13 3
## 561 80 0 13 3
## 254 80 0 2 4
## 189 80 2 28 4
## 1 80 1 8 3
## 858 80 1 10 2
## 578 80 0 7 5
## 234 80 1 16 4
## 751 80 0 2 3
## 299 80 0 4 0
## 543 80 1 1 2
## 214 80 1 8 4
## 366 80 3 36 0
## 624 80 0 9 2
## 610 80 0 3 2
## 250 80 1 22 2
## 119 80 1 13 2
## 103 80 1 6 3
## 221 80 0 6 5
## 630 80 0 9 3
## 605 80 0 2 3
## 462 80 0 15 4
## 714 80 1 16 2
## 517 80 3 1 2
## 287 80 0 1 2
## 519 80 1 10 6
## 674 80 1 10 3
## 392 80 1 5 2
## 758 80 1 20 0
## 514 80 0 15 1
## 668 80 3 8 2
## 317 80 1 6 3
## 52 80 1 32 3
## 511 80 1 10 1
## 815 80 1 3 3
## 341 80 1 6 3
## 612 80 0 10 3
## 564 80 1 13 2
## 183 80 0 18 2
## 97 80 1 23 2
## 602 80 1 9 3
## 711 80 2 9 2
## 586 80 1 15 2
## 642 80 1 15 3
## 651 80 0 3 3
## 666 80 0 10 5
## 508 80 0 14 5
## 70 80 0 17 3
## 73 80 0 8 1
## 509 80 1 13 2
## 704 80 0 6 3
## 455 80 1 21 3
## 662 80 1 14 3
## 7 80 0 7 5
## 198 80 1 22 3
## 442 80 1 9 2
## 756 80 0 9 5
## 322 80 1 10 4
## 265 80 1 6 2
## 150 80 1 6 5
## 232 80 0 2 3
## 421 80 0 5 2
## 231 80 2 10 3
## 786 80 0 9 3
## 241 80 1 10 5
## 228 80 1 4 2
## 284 80 3 13 3
## 99 80 3 4 3
## 181 80 1 15 2
## 496 80 1 29 2
## 39 80 0 36 4
## 212 80 0 21 3
## 107 80 1 10 1
## 325 80 1 21 3
## 635 80 1 10 1
## 343 80 1 9 2
## 237 80 0 24 2
## 206 80 0 8 0
## 607 80 0 20 2
## 318 80 0 4 2
## 772 80 3 24 2
## 645 80 1 5 2
## 749 80 3 6 2
## 385 80 3 18 2
## 794 80 3 17 2
## 313 80 0 7 2
## 380 80 0 7 2
## 260 80 1 7 2
## 10 80 1 8 3
## 447 80 2 19 4
## 534 80 1 9 6
## 497 80 2 8 3
## 352 80 1 10 3
## 746 80 3 2 3
## 158 80 3 21 4
## 483 80 0 16 3
## 323 80 1 10 3
## 324 80 3 19 2
## 535 80 0 7 4
## 307 80 0 8 5
## 694 80 0 3 4
## 750 80 0 4 2
## 16 80 1 9 2
## 855 80 0 4 2
## 823 80 2 8 2
## 591 80 0 4 2
## 360 80 1 16 4
## 470 80 2 5 3
## 68 80 0 5 6
## 548 80 0 10 6
## 670 80 0 36 2
## 424 80 0 0 5
## 121 80 1 10 2
## 818 80 0 14 3
## 258 80 2 8 3
## 696 80 0 3 3
## 812 80 1 7 2
## 479 80 0 12 2
## 490 80 0 10 2
## 867 80 0 4 2
## 471 80 1 6 3
## 862 80 0 23 3
## 824 80 1 2 5
## 522 80 3 1 3
## 401 80 0 11 2
## 353 80 1 16 3
## 163 80 0 6 3
## 492 80 0 25 2
## 820 80 0 6 0
## 632 80 0 14 3
## 384 80 1 3 3
## 644 80 1 5 2
## 792 80 0 9 3
## 525 80 1 7 6
## 677 80 1 10 2
## 807 80 0 6 3
## 825 80 2 7 2
## 551 80 1 22 3
## 328 80 1 9 3
## 356 80 0 15 2
## 77 80 0 10 3
## 116 80 0 10 2
## 450 80 1 10 2
## 701 80 0 1 3
## 443 80 0 5 5
## 739 80 0 5 3
## 835 80 1 34 3
## 160 80 0 20 6
## 850 80 1 8 3
## 604 80 1 10 5
## 165 80 2 12 3
## 606 80 0 32 3
## 524 80 1 31 5
## 126 80 0 10 2
## 93 80 0 7 3
## 418 80 2 4 2
## 64 80 0 0 4
## 685 80 1 5 2
## 293 80 0 7 2
## 727 80 1 10 3
## 729 80 0 1 3
## 860 80 1 10 2
## 609 80 0 8 2
## 246 80 3 8 5
## 375 80 0 2 3
## 159 80 1 18 1
## 108 80 1 35 0
## 489 80 0 23 2
## 391 80 1 6 1
## 82 80 1 17 3
## 379 80 1 6 2
## 439 80 0 3 2
## 681 80 0 5 1
## 477 80 2 9 2
## 314 80 0 10 2
## 843 80 0 31 3
## 863 80 0 9 3
## 839 80 1 28 4
## 362 80 0 1 3
## 660 80 0 6 3
## 643 80 0 31 3
## 310 80 1 4 4
## 45 80 1 1 2
## 390 80 0 3 3
## 193 80 1 19 5
## 494 80 0 11 3
## 34 80 1 10 2
## 78 80 0 27 5
## 344 80 1 1 2
## 753 80 1 32 3
## 202 80 2 10 3
## 6 80 2 9 4
## 36 80 0 5 3
## 849 80 2 8 6
## 340 80 1 1 6
## 132 80 0 8 1
## 373 80 1 24 2
## 747 80 3 4 3
## 44 80 0 24 2
## 267 80 2 16 3
## 410 80 1 22 3
## 760 80 0 26 2
## 788 80 0 11 3
## 596 80 1 9 5
## 302 80 1 9 3
## 731 80 1 4 2
## 56 80 2 5 2
## 454 80 0 10 2
## 382 80 1 8 3
## 647 80 0 0 0
## 445 80 1 24 3
## 804 80 0 9 2
## 104 80 1 5 3
## 437 80 0 5 3
## 656 80 1 5 3
## 679 80 3 18 3
## 752 80 0 3 2
## 426 80 1 18 5
## 296 80 1 5 3
## 420 80 0 10 2
## 830 80 1 32 2
## 32 80 3 14 6
## 726 80 2 5 3
## 166 80 0 2 3
## 247 80 0 11 2
## 117 80 0 5 4
## 845 80 0 6 3
## 364 80 1 22 5
## 557 80 0 19 3
## 376 80 0 32 2
## 98 80 1 17 2
## 502 80 0 6 4
## 736 80 0 9 5
## 180 80 0 12 2
## 338 80 0 7 1
## 501 80 0 31 0
## 778 80 3 1 3
## 531 80 1 20 4
## 744 80 0 3 3
## 196 80 0 1 0
## 30 80 1 5 3
## 31 80 3 9 2
## 400 80 0 16 2
## 95 80 0 21 5
## 49 80 0 0 3
## 771 80 0 9 6
## 659 80 0 8 3
## 465 80 1 6 4
## 698 80 0 16 3
## 798 80 0 1 6
## 782 80 0 20 4
## 291 80 2 8 2
## 764 80 1 19 3
## 211 80 1 21 3
## 634 80 1 7 2
## 654 80 1 15 3
## 775 80 1 24 3
## 559 80 1 10 3
## 805 80 1 17 3
## 178 80 1 6 2
## 110 80 0 4 4
## 857 80 0 8 5
## 337 80 1 5 3
## 115 80 3 15 2
## 585 80 0 9 5
## 460 80 0 10 2
## 505 80 2 26 2
## 576 80 1 7 2
## 799 80 0 1 2
## 856 80 1 1 3
## 233 80 0 25 3
## 780 80 3 4 3
## 129 80 0 20 3
## 837 80 0 8 2
## 766 80 0 10 3
## 569 80 0 10 6
## 388 80 2 8 2
## 321 80 0 5 3
## 308 80 3 6 2
## 829 80 1 9 4
## 94 80 0 10 4
## 289 80 1 10 6
## 408 80 1 7 4
## 368 80 0 9 5
## 164 80 0 10 1
## 413 80 0 8 1
## 487 80 0 1 3
## 767 80 0 10 3
## 733 80 0 1 2
## 735 80 0 1 4
## 808 80 2 2 6
## 458 80 0 11 2
## 552 80 1 6 6
## 53 80 1 10 5
## 409 80 0 31 3
## 702 80 0 6 3
## 280 80 0 4 2
## 327 80 1 23 4
## 415 80 1 3 3
## 83 80 1 10 1
## 396 80 1 3 2
## 133 80 3 7 4
## 275 80 0 36 3
## 456 80 0 21 3
## 518 80 3 7 6
## 304 80 0 10 5
## 238 80 1 6 2
## 507 80 0 1 2
## 482 80 1 8 2
## 476 80 0 14 4
## 434 80 1 1 2
## 495 80 1 10 4
## 724 80 0 15 2
## 47 80 0 10 2
## 147 80 1 8 5
## 331 80 0 23 2
## 218 80 1 24 3
## 555 80 1 5 5
## 201 80 0 22 2
## 587 80 3 17 3
## 290 80 1 3 1
## 467 80 0 7 3
## 393 80 1 4 3
## 255 80 1 16 5
## 558 80 1 6 3
## 300 80 0 5 4
## 253 80 0 1 3
## 592 80 0 16 2
## 59 80 2 15 2
## 811 80 2 12 4
## 278 80 0 9 5
## 137 80 1 1 1
## 225 80 2 10 0
## 191 80 2 11 2
## 544 80 0 14 3
## 473 80 0 17 3
## 523 80 1 25 2
## 710 80 1 31 3
## 861 80 0 8 6
## 182 80 1 11 3
## 852 80 0 10 5
## 236 80 0 17 2
## 779 80 0 7 5
## 187 80 2 7 6
## 717 80 1 10 3
## 740 80 0 9 3
## 629 80 1 23 4
## 311 80 1 9 3
## 399 80 1 13 2
## 131 80 1 10 3
## 486 80 0 8 0
## 370 80 0 10 0
## 199 80 1 24 4
## 783 80 1 7 4
## 566 80 0 6 2
## 226 80 0 12 6
## 488 80 1 5 1
## 614 80 0 1 3
## 320 80 0 1 2
## 800 80 1 13 3
## 700 80 1 25 2
## 762 80 1 26 3
## 684 80 1 12 2
## 220 80 0 8 2
## 229 80 1 4 2
## 11 80 0 21 2
## 342 80 1 2 2
## 716 80 1 1 2
## 65 80 1 1 3
## 259 80 3 9 3
## 540 80 0 9 3
## 24 80 0 10 3
## 134 80 0 7 3
## 590 80 1 4 3
## 286 80 1 15 3
## 12 80 1 3 2
## 249 80 2 12 4
## 529 80 0 6 3
## 215 80 0 5 0
## 449 80 0 10 2
## 20 80 1 10 5
## 530 80 3 7 2
## 294 80 2 14 3
## 89 80 0 9 0
## 435 80 1 11 2
## 436 80 1 8 3
## 330 80 1 21 5
## 639 80 2 6 3
## 663 80 1 13 5
## 469 80 0 7 6
## 868 80 1 11 4
## 713 80 0 7 4
## 594 80 1 10 3
## 579 80 0 10 2
## 646 80 1 15 2
## 633 80 0 19 1
## 581 80 3 7 6
## 513 80 1 10 3
## 619 80 1 9 5
## 282 80 0 20 3
## 791 80 1 10 3
## 699 80 0 0 2
## 601 80 1 1 2
## 210 80 1 12 2
## 172 80 1 16 4
## 22 80 0 12 3
## 453 80 0 10 2
## 776 80 0 2 3
## 626 80 3 5 2
## 252 80 2 12 3
## 480 80 1 16 2
## 648 80 3 3 3
## 840 80 0 29 3
## 814 80 1 27 2
## 285 80 1 7 2
## 438 80 0 8 6
## 279 80 0 6 1
## 203 80 1 14 3
## 405 80 0 6 3
## 138 80 1 22 2
## 217 80 1 18 4
## 854 80 0 9 2
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 834 3 1 1
## 697 3 2 2
## 207 2 4 3
## 715 3 10 7
## 809 2 10 0
## 725 3 0 0
## 222 3 1 1
## 71 3 5 3
## 503 3 9 7
## 403 3 2 2
## 387 4 10 7
## 806 3 12 8
## 826 3 2 2
## 821 3 3 2
## 844 2 9 7
## 528 2 6 5
## 128 2 9 8
## 539 3 5 3
## 457 3 10 0
## 732 2 1 0
## 842 2 10 9
## 85 3 16 14
## 122 3 2 2
## 474 2 6 5
## 819 3 5 3
## 625 2 0 0
## 184 3 2 0
## 92 1 11 8
## 269 3 9 7
## 463 3 2 2
## 407 3 12 7
## 235 4 25 12
## 54 2 9 7
## 146 2 4 3
## 464 2 5 4
## 192 3 3 2
## 665 3 9 5
## 394 4 18 13
## 810 2 5 2
## 512 3 6 4
## 628 3 11 10
## 248 3 2 2
## 8 3 1 0
## 468 3 1 0
## 851 3 2 2
## 404 3 3 2
## 365 3 19 9
## 491 3 3 2
## 616 4 15 10
## 748 3 13 9
## 123 3 5 4
## 841 3 0 0
## 326 3 1 1
## 149 3 4 3
## 91 3 1 0
## 72 3 5 4
## 563 3 2 2
## 80 3 1 0
## 828 3 3 2
## 481 2 5 3
## 466 3 3 2
## 510 3 22 15
## 381 3 3 2
## 90 2 2 2
## 127 3 10 2
## 737 2 20 9
## 527 4 5 4
## 100 2 5 4
## 209 3 3 2
## 148 1 2 2
## 125 2 10 7
## 754 3 8 7
## 139 1 2 2
## 649 3 15 13
## 516 3 4 1
## 3 3 2 2
## 412 3 10 8
## 667 3 11 10
## 227 3 1 0
## 4 3 14 10
## 27 3 9 8
## 240 3 14 11
## 169 3 10 9
## 422 3 13 11
## 130 3 19 17
## 784 2 10 7
## 188 3 3 2
## 521 3 5 2
## 745 2 5 3
## 256 2 3 2
## 145 3 12 11
## 262 1 9 7
## 369 3 2 2
## 573 3 7 7
## 135 4 9 7
## 152 2 10 7
## 374 1 8 4
## 822 3 3 2
## 785 1 1 0
## 446 2 6 5
## 793 4 5 4
## 17 4 8 7
## 718 4 8 7
## 176 4 2 2
## 319 4 5 4
## 372 3 9 8
## 461 3 2 2
## 361 3 33 18
## 141 3 19 16
## 28 3 5 2
## 423 2 7 7
## 363 3 32 14
## 796 3 10 8
## 859 3 5 0
## 295 3 21 7
## 574 3 10 8
## 703 2 13 2
## 334 3 9 7
## 268 3 1 0
## 272 3 11 8
## 671 4 5 2
## 51 2 0 0
## 153 2 13 8
## 441 3 8 7
## 154 1 3 2
## 87 3 7 7
## 575 4 5 3
## 414 4 0 0
## 763 3 19 2
## 58 3 10 9
## 224 1 1 0
## 580 3 6 0
## 622 4 19 10
## 155 3 1 0
## 847 3 1 0
## 542 3 10 8
## 395 3 2 1
## 67 3 17 11
## 25 3 5 0
## 288 3 2 2
## 358 4 2 2
## 827 3 1 0
## 728 4 5 3
## 730 3 2 1
## 303 3 4 2
## 69 2 3 2
## 264 3 3 1
## 562 3 2 2
## 675 3 3 2
## 427 2 1 0
## 223 3 6 5
## 35 3 1 0
## 533 3 10 7
## 239 3 2 0
## 243 3 5 4
## 707 2 7 1
## 43 2 2 2
## 777 4 5 3
## 419 2 14 8
## 333 2 5 3
## 640 3 4 3
## 757 1 10 6
## 774 2 1 0
## 864 2 22 2
## 499 2 7 7
## 484 3 2 2
## 281 3 10 9
## 84 3 7 7
## 761 3 0 0
## 37 3 13 12
## 292 3 9 8
## 537 3 1 0
## 118 3 4 3
## 261 4 1 0
## 213 3 1 0
## 459 4 3 2
## 593 4 3 2
## 448 2 0 0
## 26 3 22 9
## 599 3 6 2
## 113 3 8 6
## 345 3 4 3
## 870 3 6 2
## 177 3 5 2
## 156 4 2 2
## 568 3 10 8
## 673 3 4 2
## 432 3 4 2
## 759 2 0 0
## 440 2 9 7
## 347 2 3 2
## 833 3 6 5
## 274 2 3 2
## 40 3 2 2
## 636 4 2 2
## 2 4 20 7
## 216 4 3 1
## 312 3 20 6
## 305 3 1 0
## 838 3 10 7
## 686 4 13 8
## 162 2 10 7
## 866 3 10 9
## 832 2 0 0
## 627 3 5 4
## 329 3 20 11
## 179 3 9 8
## 545 3 6 5
## 397 1 3 2
## 185 3 8 3
## 357 2 5 4
## 170 3 7 5
## 251 2 1 1
## 638 2 8 7
## 869 2 3 2
## 429 1 3 2
## 57 3 2 2
## 705 2 2 2
## 55 3 11 7
## 853 3 9 5
## 478 3 0 0
## 797 3 20 7
## 770 3 15 10
## 257 2 2 2
## 79 3 19 7
## 66 2 6 2
## 687 3 8 7
## 339 3 0 0
## 631 4 5 2
## 676 3 11 4
## 143 3 8 7
## 561 2 8 7
## 254 3 1 0
## 189 2 10 4
## 1 2 5 2
## 858 3 10 9
## 578 3 5 2
## 234 3 6 2
## 751 3 2 2
## 299 3 3 2
## 543 2 1 1
## 214 3 4 3
## 366 2 7 7
## 624 3 9 0
## 610 2 2 2
## 250 3 18 16
## 119 3 9 8
## 103 3 3 2
## 221 3 6 5
## 630 4 8 7
## 605 3 2 2
## 462 3 4 3
## 714 3 13 9
## 517 3 1 0
## 287 3 1 0
## 519 3 9 2
## 674 2 10 2
## 392 2 2 2
## 758 2 3 2
## 514 3 15 14
## 668 3 4 3
## 317 3 4 3
## 52 3 9 8
## 511 3 10 7
## 815 3 2 2
## 341 4 1 1
## 612 3 8 7
## 564 4 11 7
## 183 3 3 2
## 97 4 13 12
## 602 2 6 5
## 711 3 9 1
## 586 4 7 2
## 642 3 7 4
## 651 3 3 0
## 666 4 3 2
## 508 3 4 2
## 70 3 17 12
## 73 3 1 0
## 509 3 9 8
## 704 3 5 1
## 455 1 3 2
## 662 3 7 3
## 7 2 4 2
## 198 3 9 8
## 442 3 5 3
## 756 3 5 2
## 322 2 10 9
## 265 3 5 3
## 150 2 5 3
## 232 3 2 2
## 421 3 5 2
## 231 3 10 7
## 786 4 3 2
## 241 2 10 9
## 228 2 4 2
## 284 3 7 7
## 99 3 3 2
## 181 4 7 7
## 496 2 20 6
## 39 3 7 3
## 212 4 1 0
## 107 3 10 7
## 325 3 21 6
## 635 2 1 0
## 343 3 3 2
## 237 2 22 6
## 206 3 8 7
## 607 2 4 3
## 318 2 4 3
## 772 3 24 13
## 645 1 1 0
## 749 4 6 2
## 385 4 10 0
## 794 3 17 14
## 313 1 7 2
## 380 3 5 2
## 260 2 2 2
## 10 2 8 2
## 447 4 13 11
## 534 3 5 1
## 497 2 2 2
## 352 2 8 7
## 746 3 1 1
## 158 3 18 7
## 483 3 15 10
## 323 2 5 4
## 324 3 16 13
## 535 2 7 7
## 307 3 1 0
## 694 3 1 0
## 750 4 3 1
## 16 2 9 8
## 855 3 3 2
## 823 3 8 7
## 591 2 4 2
## 360 3 15 13
## 470 3 4 2
## 68 4 5 2
## 548 3 1 0
## 670 3 10 9
## 424 4 0 0
## 121 3 8 7
## 818 4 13 9
## 258 3 3 2
## 696 2 3 2
## 812 2 3 2
## 479 1 5 4
## 490 3 5 1
## 867 3 3 2
## 471 2 5 3
## 862 4 21 7
## 824 2 2 2
## 522 3 1 1
## 401 3 5 4
## 353 1 1 1
## 163 3 5 0
## 492 3 3 2
## 820 3 2 0
## 632 2 14 8
## 384 3 0 0
## 644 3 5 2
## 792 3 3 2
## 525 3 1 0
## 677 3 9 7
## 807 2 3 2
## 825 3 3 2
## 551 3 22 7
## 328 2 4 3
## 356 3 13 9
## 77 3 10 9
## 116 3 10 0
## 450 3 10 7
## 701 3 1 0
## 443 1 0 0
## 739 3 5 2
## 835 3 33 9
## 160 3 1 0
## 850 3 7 7
## 604 3 10 8
## 165 2 11 9
## 606 3 5 1
## 524 3 31 9
## 126 3 10 8
## 93 3 0 0
## 418 3 1 0
## 64 1 0 0
## 685 1 5 2
## 293 2 7 7
## 727 3 5 4
## 729 1 1 0
## 860 3 10 7
## 609 3 7 7
## 246 3 4 3
## 375 3 2 2
## 159 3 8 7
## 108 3 10 9
## 489 3 1 0
## 391 3 3 2
## 82 3 7 7
## 379 2 5 4
## 439 3 3 2
## 681 4 5 2
## 477 3 1 0
## 314 3 1 0
## 843 3 9 8
## 863 2 5 2
## 839 3 22 11
## 362 3 1 0
## 660 3 6 5
## 643 4 9 7
## 310 3 3 2
## 45 3 1 0
## 390 3 3 2
## 193 2 10 7
## 494 1 11 8
## 34 3 8 0
## 78 1 26 0
## 344 3 1 0
## 753 3 30 8
## 202 3 10 4
## 6 2 9 7
## 36 1 5 1
## 849 3 6 4
## 340 3 1 0
## 132 4 5 1
## 373 3 24 9
## 747 3 2 2
## 44 3 1 0
## 267 2 14 8
## 410 3 4 1
## 760 3 24 10
## 788 2 8 2
## 596 3 9 8
## 302 2 9 8
## 731 4 1 0
## 56 3 5 3
## 454 2 10 4
## 382 3 8 7
## 647 3 0 0
## 445 3 2 1
## 804 3 8 7
## 104 4 3 2
## 437 2 2 2
## 656 3 5 3
## 679 3 4 2
## 752 4 3 2
## 426 3 1 0
## 296 3 2 2
## 420 3 10 2
## 830 3 32 5
## 32 3 14 11
## 726 3 4 2
## 166 2 2 2
## 247 2 11 9
## 117 3 4 3
## 845 3 2 2
## 364 3 21 7
## 557 3 1 0
## 376 3 32 6
## 98 4 1 0
## 502 3 4 2
## 736 2 8 7
## 180 4 8 3
## 338 2 6 2
## 501 2 10 9
## 778 3 1 0
## 531 2 19 9
## 744 1 3 2
## 196 2 1 0
## 30 3 4 3
## 31 4 8 7
## 400 2 2 2
## 95 3 5 4
## 49 3 0 0
## 771 2 7 7
## 659 3 5 4
## 465 3 1 0
## 698 4 13 11
## 798 3 1 0
## 782 3 20 7
## 291 3 5 3
## 764 3 18 7
## 211 3 20 7
## 634 3 1 0
## 654 1 0 0
## 775 1 20 8
## 559 2 9 8
## 805 4 3 2
## 178 3 3 2
## 110 3 4 2
## 857 3 5 2
## 337 3 5 2
## 115 3 7 7
## 585 4 4 2
## 460 2 10 4
## 505 4 20 17
## 576 4 7 5
## 799 4 1 1
## 856 3 1 0
## 233 1 23 5
## 780 4 2 2
## 129 1 20 7
## 837 3 6 4
## 766 3 10 7
## 569 3 5 4
## 388 4 3 2
## 321 3 3 0
## 308 3 0 0
## 829 2 6 1
## 94 3 5 2
## 289 3 9 7
## 408 3 7 7
## 368 4 8 4
## 164 3 3 2
## 413 3 1 0
## 487 2 1 0
## 767 3 10 6
## 733 3 1 0
## 735 1 1 0
## 808 3 2 2
## 458 1 10 7
## 552 3 5 1
## 53 3 10 5
## 409 3 25 8
## 702 2 6 5
## 280 3 3 2
## 327 2 22 7
## 415 2 2 2
## 83 3 8 3
## 396 1 2 2
## 133 4 6 5
## 275 3 24 15
## 456 2 21 8
## 518 2 5 3
## 304 3 10 7
## 238 2 6 4
## 507 3 1 0
## 482 3 6 4
## 476 3 9 6
## 434 3 1 0
## 495 3 10 9
## 724 2 14 8
## 47 3 0 0
## 147 3 8 7
## 331 3 8 7
## 218 3 22 17
## 555 1 3 2
## 201 2 1 0
## 587 3 13 11
## 290 2 3 2
## 467 2 3 2
## 393 4 4 2
## 255 3 9 8
## 558 3 6 5
## 300 2 3 2
## 253 3 1 0
## 592 4 15 9
## 59 3 5 2
## 811 3 12 9
## 278 3 8 7
## 137 3 1 0
## 225 4 5 2
## 191 3 10 8
## 544 3 1 0
## 473 4 0 0
## 523 3 1 0
## 710 3 31 6
## 861 2 7 0
## 182 3 7 7
## 852 3 10 0
## 236 1 14 1
## 779 3 7 7
## 187 3 7 7
## 717 3 10 3
## 740 3 3 2
## 629 3 3 2
## 311 2 2 2
## 399 2 13 12
## 131 2 10 0
## 486 1 6 4
## 370 2 7 7
## 199 3 22 6
## 783 4 4 3
## 566 3 4 3
## 226 2 12 8
## 488 2 5 2
## 614 4 1 0
## 320 3 1 0
## 800 3 5 4
## 700 3 4 2
## 762 2 26 14
## 684 1 5 3
## 220 2 8 5
## 229 2 3 2
## 11 3 16 12
## 342 3 2 2
## 716 3 1 0
## 65 1 1 0
## 259 3 5 3
## 540 4 6 4
## 24 3 5 2
## 134 3 5 4
## 590 3 2 2
## 286 3 15 14
## 12 3 3 2
## 249 2 6 2
## 529 3 6 4
## 215 3 4 2
## 449 3 10 3
## 20 3 2 0
## 530 3 5 4
## 294 1 13 8
## 89 3 9 8
## 435 2 1 0
## 436 3 8 7
## 330 3 20 7
## 639 3 6 5
## 663 2 10 6
## 469 3 2 2
## 868 2 5 4
## 713 2 1 0
## 594 2 9 6
## 579 1 9 2
## 646 2 12 11
## 633 3 1 0
## 581 2 3 2
## 513 2 5 2
## 619 3 7 7
## 282 3 18 13
## 791 3 3 2
## 699 4 0 0
## 601 3 1 0
## 210 3 9 7
## 172 4 8 7
## 22 3 2 2
## 453 3 10 7
## 776 3 2 2
## 626 3 5 3
## 252 3 7 7
## 480 3 16 11
## 648 4 3 2
## 840 3 22 10
## 814 3 5 2
## 285 4 7 6
## 438 2 7 7
## 279 3 6 4
## 203 3 14 13
## 405 2 6 5
## 138 3 10 7
## 217 3 1 0
## 854 3 8 3
## YearsSinceLastPromotion YearsWithCurrManager
## 834 0 0
## 697 2 2
## 207 1 3
## 715 0 9
## 809 0 8
## 725 0 0
## 222 0 0
## 71 1 0
## 503 7 7
## 403 2 0
## 387 0 8
## 806 5 7
## 826 2 2
## 821 0 2
## 844 1 6
## 528 0 1
## 128 3 8
## 539 1 4
## 457 0 2
## 732 0 0
## 842 0 9
## 85 5 12
## 122 1 2
## 474 0 3
## 819 1 2
## 625 0 0
## 184 2 2
## 92 3 10
## 269 2 8
## 463 2 1
## 407 5 7
## 235 4 12
## 54 0 8
## 146 0 2
## 464 1 4
## 192 2 2
## 665 8 7
## 394 13 11
## 810 1 1
## 512 0 5
## 628 2 9
## 248 2 2
## 8 0 0
## 468 0 0
## 851 2 2
## 404 0 2
## 365 9 11
## 491 1 2
## 616 4 13
## 748 4 9
## 123 0 2
## 841 0 0
## 326 0 0
## 149 1 2
## 91 0 0
## 72 1 3
## 563 2 2
## 80 0 0
## 828 0 2
## 481 0 2
## 466 1 2
## 510 15 8
## 381 1 1
## 90 2 1
## 127 6 7
## 737 9 6
## 527 0 3
## 100 0 2
## 209 2 2
## 148 2 2
## 125 0 9
## 754 0 7
## 139 2 2
## 649 2 8
## 516 1 2
## 3 2 2
## 412 8 7
## 667 4 1
## 227 0 0
## 4 5 7
## 27 7 7
## 240 1 11
## 169 6 9
## 422 10 7
## 130 2 8
## 784 0 1
## 188 1 2
## 521 1 3
## 745 0 4
## 256 1 2
## 145 11 8
## 262 0 7
## 369 2 2
## 573 0 7
## 135 0 0
## 152 0 8
## 374 7 7
## 822 0 2
## 785 0 0
## 446 1 2
## 793 0 2
## 17 6 3
## 718 1 7
## 176 2 2
## 319 1 4
## 372 7 8
## 461 2 0
## 361 11 9
## 141 15 9
## 28 0 3
## 423 7 7
## 363 6 9
## 796 9 7
## 859 1 4
## 295 7 7
## 574 4 8
## 703 4 12
## 334 0 7
## 268 0 0
## 272 7 9
## 671 0 2
## 51 0 0
## 153 4 8
## 441 0 7
## 154 1 2
## 87 1 7
## 575 1 4
## 414 0 0
## 763 11 9
## 58 1 5
## 224 0 0
## 580 1 0
## 622 2 7
## 155 1 0
## 847 0 0
## 542 7 6
## 395 2 2
## 67 11 8
## 25 1 4
## 288 1 2
## 358 1 1
## 827 0 0
## 728 2 0
## 730 2 1
## 303 1 2
## 69 1 2
## 264 0 2
## 562 2 2
## 675 0 2
## 427 0 0
## 223 1 2
## 35 0 0
## 533 0 7
## 239 1 2
## 243 0 4
## 707 1 7
## 43 2 2
## 777 1 3
## 419 3 11
## 333 2 3
## 640 0 2
## 757 0 7
## 774 0 0
## 864 11 13
## 499 7 7
## 484 2 2
## 281 8 8
## 84 6 7
## 761 0 0
## 37 6 0
## 292 0 8
## 537 0 0
## 118 0 3
## 261 0 0
## 213 0 0
## 459 1 0
## 593 1 2
## 448 0 0
## 26 3 10
## 599 0 3
## 113 4 0
## 345 0 3
## 870 1 2
## 177 1 4
## 156 2 2
## 568 6 7
## 673 1 3
## 432 0 0
## 759 0 0
## 440 1 8
## 347 0 2
## 833 3 3
## 274 0 2
## 40 2 2
## 636 2 2
## 2 4 9
## 216 1 2
## 312 5 13
## 305 0 0
## 838 5 7
## 686 0 8
## 162 1 9
## 866 0 8
## 832 0 0
## 627 0 3
## 329 0 7
## 179 3 7
## 545 1 3
## 397 0 1
## 185 0 7
## 357 4 3
## 170 1 7
## 251 0 0
## 638 1 1
## 869 0 2
## 429 1 2
## 57 2 2
## 705 2 2
## 55 1 8
## 853 0 8
## 478 0 0
## 797 4 10
## 770 4 12
## 257 2 2
## 79 12 8
## 66 0 5
## 687 7 7
## 339 0 0
## 631 0 4
## 676 0 8
## 143 0 7
## 561 7 2
## 254 0 0
## 189 1 6
## 1 0 3
## 858 8 9
## 578 0 3
## 234 0 5
## 751 0 2
## 299 1 2
## 543 0 1
## 214 0 2
## 366 7 7
## 624 1 7
## 610 2 2
## 250 11 8
## 119 1 8
## 103 1 2
## 221 1 4
## 630 7 7
## 605 2 2
## 462 1 3
## 714 1 12
## 517 0 0
## 287 0 0
## 519 6 7
## 674 7 8
## 392 0 0
## 758 1 2
## 514 8 12
## 668 1 3
## 317 1 2
## 52 1 5
## 511 1 9
## 815 1 2
## 341 0 0
## 612 0 7
## 564 4 8
## 183 1 2
## 97 5 1
## 602 1 2
## 711 0 8
## 586 3 7
## 642 7 7
## 651 1 2
## 666 0 2
## 508 3 2
## 70 5 7
## 73 0 1
## 509 5 8
## 704 1 4
## 455 0 2
## 662 5 7
## 7 0 3
## 198 2 3
## 442 1 2
## 756 1 4
## 322 8 8
## 265 1 2
## 150 0 0
## 232 2 2
## 421 1 3
## 231 3 9
## 786 1 0
## 241 5 8
## 228 2 2
## 284 1 7
## 99 0 2
## 181 6 4
## 496 4 17
## 39 7 7
## 212 0 0
## 107 0 9
## 325 11 8
## 635 0 0
## 343 1 2
## 237 4 14
## 206 7 1
## 607 1 3
## 318 1 2
## 772 15 7
## 645 0 0
## 749 1 2
## 385 2 7
## 794 12 8
## 313 7 7
## 380 0 1
## 260 0 2
## 10 7 7
## 447 2 9
## 534 1 2
## 497 0 2
## 352 7 7
## 746 0 0
## 158 11 5
## 483 6 11
## 323 0 3
## 324 1 7
## 535 0 7
## 307 0 0
## 694 0 0
## 750 0 2
## 16 0 0
## 855 0 2
## 823 1 7
## 591 1 3
## 360 10 11
## 470 1 3
## 68 1 4
## 548 0 0
## 670 0 9
## 424 0 0
## 121 0 5
## 818 3 7
## 258 0 2
## 696 1 2
## 812 0 2
## 479 0 4
## 490 4 3
## 867 2 2
## 471 1 2
## 862 15 17
## 824 2 2
## 522 0 0
## 401 0 2
## 353 0 0
## 163 1 2
## 492 1 2
## 820 2 2
## 632 2 1
## 384 0 0
## 644 3 0
## 792 1 2
## 525 0 0
## 677 0 5
## 807 1 2
## 825 1 1
## 551 2 10
## 328 1 2
## 356 3 12
## 77 8 6
## 116 0 9
## 450 4 5
## 701 0 0
## 443 0 0
## 739 1 0
## 835 15 0
## 160 0 0
## 850 7 7
## 604 0 8
## 165 6 9
## 606 1 3
## 524 0 9
## 126 0 9
## 93 0 0
## 418 0 0
## 64 0 0
## 685 0 2
## 293 0 3
## 727 0 0
## 729 0 0
## 860 0 5
## 609 0 7
## 246 0 3
## 375 0 2
## 159 0 1
## 108 1 4
## 489 0 0
## 391 1 2
## 82 0 7
## 379 1 3
## 439 0 2
## 681 0 3
## 477 0 0
## 314 0 0
## 843 0 0
## 863 0 4
## 839 14 10
## 362 0 0
## 660 0 3
## 643 6 2
## 310 1 2
## 45 0 1
## 390 1 2
## 193 0 8
## 494 3 3
## 34 1 7
## 78 0 12
## 344 0 0
## 753 12 13
## 202 0 9
## 6 1 7
## 36 0 3
## 849 1 0
## 340 0 0
## 132 0 4
## 373 9 11
## 747 2 0
## 44 0 1
## 267 6 9
## 410 1 0
## 760 1 11
## 788 7 7
## 596 0 8
## 302 1 8
## 731 0 0
## 56 0 4
## 454 0 9
## 382 5 7
## 647 0 0
## 445 2 2
## 804 4 7
## 104 0 2
## 437 2 1
## 656 3 3
## 679 0 2
## 752 2 2
## 426 0 0
## 296 2 2
## 420 0 7
## 830 10 7
## 32 2 13
## 726 1 2
## 166 0 2
## 247 4 10
## 117 1 1
## 845 2 2
## 364 3 9
## 557 0 0
## 376 13 9
## 98 0 0
## 502 0 3
## 736 0 7
## 180 0 7
## 338 0 4
## 501 5 9
## 778 0 0
## 531 1 9
## 744 1 2
## 196 0 0
## 30 1 2
## 31 0 7
## 400 2 2
## 95 4 4
## 49 0 0
## 771 0 1
## 659 1 2
## 465 0 0
## 698 3 7
## 798 0 0
## 782 11 10
## 291 0 2
## 764 0 13
## 211 0 10
## 634 0 0
## 654 0 0
## 775 13 9
## 559 7 8
## 805 1 2
## 178 1 2
## 110 1 2
## 857 0 4
## 337 0 2
## 115 1 7
## 585 1 3
## 460 1 8
## 505 5 6
## 576 0 7
## 799 0 0
## 856 0 0
## 233 14 10
## 780 2 2
## 129 1 8
## 837 1 3
## 766 1 4
## 569 0 3
## 388 1 2
## 321 0 2
## 308 0 0
## 829 0 5
## 94 0 4
## 289 2 8
## 408 1 7
## 368 7 1
## 164 0 2
## 413 0 0
## 487 0 0
## 767 0 8
## 733 0 0
## 735 0 0
## 808 2 2
## 458 1 0
## 552 0 4
## 53 7 7
## 409 3 7
## 702 1 3
## 280 1 2
## 327 1 10
## 415 1 0
## 83 7 7
## 396 2 2
## 133 0 4
## 275 2 15
## 456 12 8
## 518 0 4
## 304 0 8
## 238 0 5
## 507 0 0
## 482 0 2
## 476 0 8
## 434 0 0
## 495 1 7
## 724 7 8
## 47 0 0
## 147 1 6
## 331 0 0
## 218 4 7
## 555 0 2
## 201 0 0
## 587 1 9
## 290 0 2
## 467 1 2
## 393 2 3
## 255 4 8
## 558 1 3
## 300 2 2
## 253 0 0
## 592 10 10
## 59 0 2
## 811 6 10
## 278 1 7
## 137 0 0
## 225 0 3
## 191 1 9
## 544 0 0
## 473 0 0
## 523 0 0
## 710 14 7
## 861 7 7
## 182 1 7
## 852 1 8
## 236 11 7
## 779 5 7
## 187 0 7
## 717 1 4
## 740 0 2
## 629 1 2
## 311 2 1
## 399 1 9
## 131 0 9
## 486 0 5
## 370 0 7
## 199 5 17
## 783 0 3
## 566 1 2
## 226 1 7
## 488 4 3
## 614 0 0
## 320 0 1
## 800 0 4
## 700 0 3
## 762 3 0
## 684 1 4
## 220 2 2
## 229 0 2
## 11 6 14
## 342 2 2
## 716 0 0
## 65 0 0
## 259 1 0
## 540 1 5
## 24 0 3
## 134 1 0
## 590 2 2
## 286 5 7
## 12 2 2
## 249 3 3
## 529 0 4
## 215 1 1
## 449 9 9
## 20 2 2
## 530 4 3
## 294 5 12
## 89 1 7
## 435 0 0
## 436 0 7
## 330 0 9
## 639 1 4
## 663 0 3
## 469 2 2
## 868 1 2
## 713 0 0
## 594 1 4
## 579 3 8
## 646 2 11
## 633 0 0
## 581 1 2
## 513 1 3
## 619 1 7
## 282 2 17
## 791 1 2
## 699 0 0
## 601 0 0
## 210 0 7
## 172 1 7
## 22 2 2
## 453 7 7
## 776 0 2
## 626 0 3
## 252 0 7
## 480 6 8
## 648 0 2
## 840 12 9
## 814 1 0
## 285 5 0
## 438 7 6
## 279 0 3
## 203 6 8
## 405 1 1
## 138 0 8
## 217 0 0
## 854 0 7
ddsTest = DDSAnalytics[-ddsObs,]
DDSAnalytics$YearsAtCompany_log = log(DDSAnalytics$YearsAtCompany)
DDSAnalytics$MonthlyRate_log = log(DDSAnalytics$MonthlyRate)
DDSAnalytics$NumCompaniesWorked_log = log(DDSAnalytics$NumCompaniesWorked) # many -inf no use
# fitting or training our model on Training Set
ddsModel <- lm(MonthlyIncome ~ Age + NumCompaniesWorked + JobSatisfaction+JobLevel+
YearsAtCompany +TotalWorkingYears, data = ddsTrain) # train data model
summary(ddsModel)
##
## Call:
## lm(formula = MonthlyIncome ~ Age + NumCompaniesWorked + JobSatisfaction +
## JobLevel + YearsAtCompany + TotalWorkingYears, data = ddsTrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5709.8 -841.0 58.5 719.4 3827.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1624.093 291.344 -5.574 3.65e-08 ***
## Age -3.971 8.175 -0.486 0.62728
## NumCompaniesWorked -18.279 23.638 -0.773 0.43964
## JobSatisfaction 6.326 47.176 0.134 0.89337
## JobLevel 3665.519 77.093 47.547 < 2e-16 ***
## YearsAtCompany -40.092 13.134 -3.053 0.00236 **
## TotalWorkingYears 91.067 16.487 5.524 4.82e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1340 on 645 degrees of freedom
## Multiple R-squared: 0.9191, Adjusted R-squared: 0.9184
## F-statistic: 1221 on 6 and 645 DF, p-value: < 2.2e-16
dfres <- data.frame(ddsModel$residuals)
dfres%>%ggplot(aes(ddsModel.residuals,fill="#DC143C"))+geom_histogram(color="white")+ggtitle("Residual Histogram")+xlab("Residuals")+
theme(panel.background = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#hist(ddsModel$residuals,col="#1E90FF",main = "Residual Histogram")
plot(ddsModel$residuals,col="#F8766D",main = "Residual Plots")
library(ggpubr)
ggqqplot(ddsModel$residuals)
# #1E90FF", "#DC143C
# Testing our model built using train set with the test data set
ddsTest$MonthlyIncomePred = predict(ddsModel, newdata = ddsTest)
# MS_PE = data.frame(Observed = ddsTest$MonthlyIncome, Predicted = ddsTest$MonthlyIncomePred)
# # calculating MSPE to see how good our model is.
# MS_PE$Residual = MS_PE$Observed - MS_PE$Predicted
# MS_PE$SquaredResidual = MS_PE$Residual^2
#
# mean(MS_PE$SquaredResidual)
# RMSE(MS_PE$Observed,MS_PE$Predicted)
# I have also provided an additional data set of 300 observations that do not have the Monthly Incomes. This data is in the file “CaseStudy2CompSet No Salary.csv”.
# Importing No Salary data to predict the salary
DDSAnalyticsNoSalary <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Salary.csv",sep = ",",header = TRUE)
# Use model to predict the unkown Montly Income from dataset.
ddsNoSalPreds = predict(ddsModel,newdata = DDSAnalyticsNoSalary)
as.data.frame(ddsNoSalPreds)->ddsAnalyticSalPreds
names(ddsAnalyticSalPreds) <- c("Predicted Salary")
head(ddsAnalyticSalPreds)
## Predicted Salary
## 1 5900.026
## 2 2215.897
## 3 14152.723
## 4 2147.704
## 5 2279.071
## 6 7212.705
DDSAnalyticsNoSalary$PredictedMonthlyIncome = ddsNoSalPreds #add PredictedSalary col and put the predicted values into tht column.
library(dplyr)
DDSAnalyticsNoSalary%>%dplyr::select(ID,PredictedMonthlyIncome)->PredictionWithID
head(PredictionWithID) # dataframe with ID and PredictedMonthlyIncome.
## ID PredictedMonthlyIncome
## 1 871 5900.026
## 2 872 2215.897
## 3 873 14152.723
## 4 874 2147.704
## 5 875 2279.071
## 6 876 7212.705
write.csv(PredictionWithID,"DDSIncomePrediction.csv") # write csv file to Excel
Turnover Analysis
#Turnover Analysis
#https://corpgov.law.harvard.edu/2018/01/22/the-option-to-quit-the-effect-of-employee-stock-options-on-turnover/
library(plyr)
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following object is masked from 'package:ggpubr':
##
## mutate
## The following object is masked from 'package:maps':
##
## ozone
## The following object is masked from 'package:purrr':
##
## compact
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
library(dplyr)
library(tidyverse)
# Donut chart Attrition Percentage
Att <- DDSAnalytics%>%group_by(Attrition)%>%
dplyr::summarize(count=n(),
percentage = n()/nrow(DDSAnalytics))
Att
## # A tibble: 2 x 3
## Attrition count percentage
## <fct> <int> <dbl>
## 1 No 730 0.839
## 2 Yes 140 0.161
Attdonut <- ggplot(data = Att,aes(x=2,y=percentage,fill=Attrition))+
geom_col(color="white")+
coord_polar("y", start = 1)+
geom_text(aes(label=paste0(round(percentage*100),"%")),
position = position_stack(vjust = 0.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Attrition Percentage")+
xlim(0.5,2.5)
Attdonut
# Donut chart Marital Sttus percentage
ddsMaritalStat <- DDSAnalytics%>%group_by(MaritalStatus)%>%
dplyr::summarize(count=n(),
Mpercentage=n()/nrow(DDSAnalytics))
ddsMaritalStatDonut <- ggplot(data = ddsMaritalStat,aes(x=2,y=Mpercentage,fill=MaritalStatus))+
geom_col(color="white")+
coord_polar("y",start=1)+
geom_text(aes(label=paste0(round(Mpercentage*100),"%")),
position = position_stack(vjust = 0.5),color="white",size=I(4.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Marital Status Percentage")+
xlim(0.3,2.5)
ddsMaritalStatDonut
DDSAnalytics$StockOptionLevel = as.factor(DDSAnalytics$StockOptionLevel)
DDSAnalytics$JobInvolvement = as.factor(DDSAnalytics$JobInvolvement)
#JobInvolvement Donut chart percentage
ddsJobInv <- DDSAnalytics%>%group_by(JobInvolvement)%>%
dplyr::summarize(count=n(),
InvPercentage = n()/nrow(DDSAnalytics))
ddsJobInvDonut <- ggplot(data = ddsJobInv, aes(x=2,y=InvPercentage,fill=JobInvolvement))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(InvPercentage*100),"%")),
position=position_stack(vjust = 0.5),size=I(5.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Job Involvement Percentage")+
xlim(0.5,2.5)
ddsJobInvDonut
# Attrition based on Job Involvement bar chart
DDSAnalytics%>%ggplot(aes(x=JobInvolvement,fill=Attrition))+geom_bar()+
ggtitle("Involvement Keeps Employees In The Company")+
xlab("Job Involvement")+ylab("Employee Count")+
theme(panel.background = element_blank(),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size = I(12)),
axis.title = element_text(size=I(15)))
#panel.background = element_rect(fill = "lightblue",
# colour = "lightblue",
# Stock Option Level pie chart
ddsStockOpt <- DDSAnalytics%>%group_by(StockOptionLevel)%>%
dplyr::summarize(count=n(),
StockPercentage = n()/nrow(DDSAnalytics))
ddsStockPie <- ggplot(data=ddsStockOpt,aes(x=2,y=StockPercentage,fill=StockOptionLevel))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(StockPercentage*100),"%")),
position = position_stack(vjust = 0.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_text(hjust = 0.5,size = I(19)))+
ggtitle("Percent Of Employees Owning Stock Option")
ddsStockPie
# Bar Chart Marital Status and Attrition Relationship
DDSAnalytics%>%ggplot(aes(MaritalStatus))+geom_bar(aes(fill=Attrition))+
ggtitle("Higher Attrition Among Sinlge Employee")+xlab("Marital Status")+
theme(plot.title = element_text(hjust = 0.5,size = I(17)))->AttvsMart
AttvsMart
figure <- ggarrange(Attdonut,ddsMaritalStatDonut,AttvsMart,
labels = c("A", "B","C"),
ncol = 3, nrow = 1)
figure
# Donut chart Relationship StockOptionLevel Attrition 'Yes'
DDSAnalytics%>%filter(Attrition=="Yes")->AttYes
AttYes%>%ggplot(aes(Attrition))+geom_bar(aes(fill=StockOptionLevel))+
ggtitle("Employe Attrition Based On Stock Option ")+
theme(plot.title = element_text(hjust = 0.5,size = I(20)),
axis.text = element_text(size=I(17)),
axis.title = element_text(size=I(18)),
panel.background = element_blank(),
axis.title.y = element_blank()
)
DDSAnalytics%>%ggplot(aes(StockOptionLevel))+geom_bar(aes(fill=MaritalStatus))+
ggtitle("Not Enough Stock Option For Single Employee ")+
theme(plot.title = element_text(hjust = 0.5,size = I(20)),
axis.text = element_text(size=I(17)),
axis.title = element_text(size=I(18)),
panel.background = element_blank(),
axis.title.y = element_blank()
)
library(dplyr)
DDSAnalytics%>%ggplot(aes(JobInvolvement, fill= Attrition))+
geom_bar() +
ggtitle("Age Group Attrition ")+
theme(plot.title = element_text(hjust = 0.5,size = I(20)),
axis.text = element_text(size=I(17)),
axis.title = element_text(size=I(18)),
panel.background = element_blank()
)
DDSAnalytics%>%ggplot(aes(x=JobRole,fill=Attrition))+
geom_bar()
DDSAnalytics%>%ggplot(aes(y=MonthlyIncome,x=Attrition,fill=Attrition))+
geom_boxplot()+ggtitle("Lower Income Cause Of Attrition")+
xlab("Attrition")+ylab("Monthly Income")+
theme(panel.background =element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15)))
#Lower Monthlty Cause Of Attrition
DDSAnalytics%>%ggplot(aes(y=MonthlyRate,x=Attrition,fill=Attrition))+
geom_boxplot()+ggtitle("Lower Monthlty Rate Cause Of Attrition")+
xlab("Attrition")+ylab("Monthly Rate")+
theme(panel.background =element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15)),
legend.position = "none")
# Monthly Income Attrition Jobrole boxplot
DDSAnalytics%>%ggplot(aes(y=MonthlyIncome,x=Attrition,fill=JobRole))+geom_boxplot()+
ggtitle("Job Role Income Analysis")+
xlab("Attrition")+ylab("Monthly Income")+
theme(panel.grid.major = element_line(color="grey70",size=0.2),
panel.background = element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(18)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15))
)
#MonthlyIncome,Attrition,JobRole ggpairs
DDSAnalytics%>%dplyr::select(MonthlyIncome,Attrition,JobRole)%>%
ggpairs(aes(color=JobRole))+
ggtitle("Job Role Income Analysis Based On Attrition")+
xlab("Attrition")+ylab("Monthly Income")+
theme(panel.grid.major = element_line(color="grey70",size=0.2),
panel.background = element_rect(fill = "white", colour = "grey50"),
plot.title = element_text(hjust = 0.5,size=I(24)),
axis.text = element_text(size=I(13)),
axis.title = element_text(size=I(15))
)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Subseting data set based on Attrtion YES and NO
DDSAnalytics%>%ggplot(aes(OverTime))+geom_bar(aes(fill=Attrition))
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=YearsSinceLastPromotion))+geom_point(aes(color=Attrition))
DDSAnalytics%>%ggplot(aes(Gender))+geom_bar(aes(fill=Attrition))
DDSAnalytics%>%filter(Attrition=="Yes")->AttYes
DDSAnalytics%>%filter(Attrition=="No")->AttNo
library(dplyr)
#Job Involvement Rating Percentage Attrition YES
AttYes$JobInvolvement = as.factor(AttYes$JobInvolvement)
AttYesJobInvCt <- AttYes%>%group_by(JobInvolvement)%>%
dplyr::summarize(count=n(),
JobInvPercent = n()/nrow(AttYes))
jobInvYesAtt <- ggplot(data = AttYesJobInvCt,aes(x=2,y=JobInvPercent,fill=JobInvolvement))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(JobInvPercent*100),"%")),
position = position_stack(vjust = 0.5),size=I(5.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "none",
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Job Involvement Rating Percentage Attrition \nYes")+
xlim(0.5,2.5)
jobInvYesAtt
#Job Involvement Rating Percentage Attrition NO
AttNo$JobInvolvement = as.factor(AttNo$JobInvolvement)
AttNoJobInvCt <- AttNo%>%group_by(JobInvolvement)%>%
dplyr::summarize(count=n(),
JobInvPercentAttNo = n()/nrow(AttNo))
jobInvNoAttDonut <- ggplot(data = AttNoJobInvCt,aes(x=2,y=JobInvPercentAttNo,fill=JobInvolvement))+
geom_col(color="white")+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(JobInvPercentAttNo*100),"%")),
position = position_stack(vjust = 0.5),size=I(4.5))+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "none",
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("Job Involvement Rating Percentage Attrition \nNo")+
xlim(0.5,2.5)
jobInvNoAttDonut
# Attrition Yes Over Time Analysis
AttYesOvrtm <- AttYes%>%group_by(OverTime)%>%
dplyr::summarize(count=n(),
OverTmPercent = n()/nrow(AttYes))
attOverTime <- ggplot(data = AttYesOvrtm,aes(x=3,y=OverTmPercent,fill=OverTime))+
geom_col(color="white")+
scale_fill_manual(values=c("#1E90FF", "#DC143C"))+
coord_polar("y",start=1)+
geom_text(aes(label=paste0(round(OverTmPercent*100),"%")),
position = position_stack(vjust = 0.5),size=I(6),color="white")+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.text = element_text(size = I(13)),
plot.title = element_text(hjust = 0.5,size=I(19))
)+
ggtitle("Over Time And Attrition \n YES")+
xlim(0.1,3.5)
attOverTime
# N0 Attrition vs Over Time
AttNoOvtm <- AttNo%>%group_by(OverTime)%>%
dplyr::summarize(count=n(),
OverTmPercentNatt = n()/nrow(AttNo))
attOverTimeNatt <- ggplot(data = AttNoOvtm,aes(x=3,y=OverTmPercentNatt,fill=OverTime))+
geom_col(color="white")+
scale_fill_manual(values = c("#1E90FF", "#DC143C"))+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(OverTmPercentNatt*100),"%")),
position = position_stack(vjust = 0.5),size=I(6),color="white")+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(19)))+
ggtitle("Over Time And Attrition \n NO")+
xlim(0.1,3.5)
attOverTimeNatt
#Q. 4. The business is also interested in learning about any job role specific trends that may exist in the data set (e.g., “Data Scientists have the highest job satisfaction”).
as.factor(DDSAnalytics$JobSatisfaction)->jobsatFac
str(jobsatFac)
## Factor w/ 4 levels "1","2","3","4": 4 3 4 4 4 1 3 4 3 3 ...
dfjobSat <- data.frame(jRol=DDSAnalytics$JobRole,Satisfaction=jobsatFac)
# ordering bars in ascending order
dfjobSat <- within(dfjobSat,
jRol <- factor(jRol,levels = names(sort(table(jRol),decreasing = FALSE))))
ggplot(dfjobSat,aes(x=jRol))+geom_bar(aes(fill=Satisfaction))+ggtitle("Job Role Vs Job Satisfaction")+
xlab("Job Role")+ylab("Number of Employee ")+coord_flip()+theme(plot.title = element_text(
color="Black",
hjust = 0.5,
size = 18,
),axis.text.y = element_text(color="Black",
size = 15),
axis.text.x = element_text(color="black",
size=15),
axis.title.x = element_text(size = 17),
axis.title.y = element_text(size = 17),
panel.background = element_blank()
)-> df2
df2
##Q.5. You can also provide any other interesting trends and observations from your analysis.
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=Age,color=JobRole))+
geom_point(aes(size=I(2.3)))+ggtitle("Scatter Plot Of Job Role Based On Age And Years At The Company")+
labs(x="Years at the company",y="Employee Age")+theme(
plot.title = element_text(color = "#000000",
size = 12,
hjust = 0.5),
axis.text = element_text(color = "#000000",
size = 14),
axis.title = element_text(color = "#000000",
size=16),
legend.text = element_text(color = "#000000",
size = 13),
panel.background = element_blank()
)
Attrition Classifcation Section
#DDSAnalyticsNoAttrition <-read.csv(file.choose())
DDSAnalyticsNoAttritionknn <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv",sep=",",header = TRUE)
DDSAnalyticsCfknn <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
DDSAnalyticsCfknn%>%dplyr::select(MonthlyIncome, Age, NumCompaniesWorked,JobSatisfaction,JobLevel,YearsAtCompany,
TotalWorkingYears)->cl
clCor <- cor(cl)
corrplot(clCor,method = "number")
DDSAnalyticsCfknn$OverTime = as.numeric(DDSAnalyticsCfknn$OverTime)
DDSAnalyticsCfknn$Gender = as.numeric(DDSAnalyticsCfknn$Gender)
DDSAnalyticsCfknn$BusinessTravel= as.numeric(DDSAnalyticsCfknn$BusinessTravel)
DDSAnalyticsCfknn$MaritalStatus = as.numeric(DDSAnalyticsCfknn$MaritalStatus)
DDSAnalyticsCfknn$JobRole = as.numeric(DDSAnalyticsCfknn$JobRole)
DDSAnalyticsCfknn$MonthlyIncome_log = log(DDSAnalyticsCfknn$MonthlyIncome)
DDSAnalyticsCfknn$MonthlyRate_log = log(DDSAnalyticsCfknn$MonthlyRate)
kNN Classification
DDSAnalyticsCfknn$OverTime <- ifelse(DDSAnalyticsCfknn$OverTime=="yes",1,0)
mstatus <- as.data.frame(dummy.code(DDSAnalyticsCfknn$MaritalStatus))
jRol <- as.data.frame(dummy.code(DDSAnalyticsCfknn$JobRole))
knnclsDataframe <- data.frame(Attrition=DDSAnalyticsCfknn$Attrition,
JobInvolvement=DDSAnalyticsCfknn$JobInvolvement,
JobRole=jRol,
MaritalStatus = mstatus,
OverTime = DDSAnalyticsCfknn$OverTime,
StockOptionLevel= DDSAnalyticsCfknn$StockOptionLevel,
JobLevel=DDSAnalyticsCfknn$JobLevel,
JobSatisfaction=DDSAnalyticsCfknn$JobSatisfaction,
YearsAtCompany =DDSAnalyticsCfknn$YearsAtCompany,
YearsInCurrentRole=DDSAnalyticsCfknn$YearsInCurrentRole)
DDSAnalyticsNoAttritionknn$OverTime <- ifelse(DDSAnalyticsNoAttritionknn$OverTime=="yes",1,0)
mstatus <- as.data.frame(dummy.code(DDSAnalyticsNoAttritionknn$MaritalStatus))
jRol <- as.data.frame(dummy.code(DDSAnalyticsNoAttritionknn$JobRole))
knnPredictionDataframe <- data.frame(JobInvolvement = DDSAnalyticsNoAttritionknn$JobInvolvement,
JobRole = jRol,
MaritalStatus = mstatus,
OverTime = DDSAnalyticsNoAttritionknn$OverTime,
StockOptionLevel = DDSAnalyticsNoAttritionknn$StockOptionLevel,
JobLevel = DDSAnalyticsNoAttritionknn$JobLevel,
JobSatisfacton= DDSAnalyticsNoAttritionknn$JobSatisfaction,
yearAtCompany = DDSAnalyticsNoAttritionknn$YearsAtCompany)
#kNN Classification cross validation
# spliting data set knnclsDataframe
set.seed(7)
ddsObsknn = sample(seq(1,dim(knnclsDataframe)[1]),round(.75*dim(knnclsDataframe)[1]),replace = FALSE)
ddsknnTrain = knnclsDataframe[ddsObsknn,]
ddsknnTest = knnclsDataframe[-ddsObsknn,]
# kNN crossvalidation
AttClassification=knn(ddsknnTrain[,2:19],ddsknnTest[,2:19],ddsknnTrain$Attrition,prob = TRUE,k=17)
table(AttClassification,ddsknnTest$Attrition)
##
## AttClassification No Yes
## No 184 21
## Yes 5 8
caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
## Confusion Matrix and Statistics
##
##
## AttClassification No Yes
## No 184 21
## Yes 5 8
##
## Accuracy : 0.8807
## 95% CI : (0.8301, 0.9206)
## No Information Rate : 0.867
## P-Value [Acc > NIR] : 0.315662
##
## Kappa : 0.3254
##
## Mcnemar's Test P-Value : 0.003264
##
## Sensitivity : 0.9735
## Specificity : 0.2759
## Pos Pred Value : 0.8976
## Neg Pred Value : 0.6154
## Prevalence : 0.8670
## Detection Rate : 0.8440
## Detection Prevalence : 0.9404
## Balanced Accuracy : 0.6247
##
## 'Positive' Class : No
##
kCM = caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
kCM
## Confusion Matrix and Statistics
##
##
## AttClassification No Yes
## No 184 21
## Yes 5 8
##
## Accuracy : 0.8807
## 95% CI : (0.8301, 0.9206)
## No Information Rate : 0.867
## P-Value [Acc > NIR] : 0.315662
##
## Kappa : 0.3254
##
## Mcnemar's Test P-Value : 0.003264
##
## Sensitivity : 0.9735
## Specificity : 0.2759
## Pos Pred Value : 0.8976
## Neg Pred Value : 0.6154
## Prevalence : 0.8670
## Detection Rate : 0.8440
## Detection Prevalence : 0.9404
## Balanced Accuracy : 0.6247
##
## 'Positive' Class : No
##
set.seed(1)
iterations = 100
numks = 60
splitPerc = .95
masterAcc = matrix(nrow = iterations, ncol = numks)
for(j in 1:iterations)
{
ddsObsknn = sample(seq(1,dim(knnclsDataframe)[1]),round(.75*dim(knnclsDataframe)[1]),replace = FALSE)
ddsknnTrain = knnclsDataframe[ddsObsknn,]
ddsknnTest = knnclsDataframe[-ddsObsknn,]
for(i in 1:numks)
{
AttClassification=knn(ddsknnTrain[,2:19],ddsknnTest[,2:19],ddsknnTrain$Attrition,prob = TRUE,k=i)
table(AttClassification,ddsknnTest$Attrition)
kCM = caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
masterAcc[j,i] = kCM$overall[1]
}
}
MeanAcc = colMeans(masterAcc)
plot(seq(1,numks,1),MeanAcc, type = "l")
which.max(MeanAcc)
## [1] 18
max(MeanAcc)
## [1] 0.8431193
# # normalizing the values because Knn does the classification based on distances and it does better job when all the variables are normalized and are in same scalling.
# normalize <- function(x){
# return((x-min(x))/(max(x)-min(x)))
# }
# #for example normalize(c(1,2,3,4,5))
# # for exampl normalize(c(20,30,40,50,100))
# # lets apply the function to the variables we want to normalize
# # e.g: iris_n <- as.data.frame (lapply(iris[,c(1,2,3,4)],normalize))
#
# DDSAnalytics_normalized = as.data.frame(lapply(DDSAnalyticsCf[c(18,32,35,36,22,29,15,7)],normalize))
# summary(DDSAnalytics_normalized)
# dataFrame1 <- data.frame(
# YearsSinceLastPromotion = DDSAnalytics_normalized$YearsSinceLastPromotion,
# StockOptionLevel = DDSAnalytics_normalized$StockOptionLevel,
# NumCompaniesWorked = DDSAnalytics_normalized$NumCompaniesWorked,
# JobSatisfaction= DDSAnalytics_normalized$JobSatisfaction,
# JobInvolvement = DDSAnalytics_normalized$JobInvolvement,
# DistanceFromHome = DDSAnalytics_normalized$DistanceFromHome,
# Attrition = DDSAnalyticsCf$Attrition,
# Gender = DDSAnalyticsCf$Gender,
# MaritalStatus = DDSAnalyticsCf$MaritalStatus,
# BusinessTravelTravel= DDSAnalyticsCf$BusinessTravel)
#
# library(class)
# library(caret)
# DDSAnalyticsCf$OverTime = as.numeric(DDSAnalyticsCf$OverTime)
# DDSAnalyticsCf$Gender = as.numeric(DDSAnalyticsCf$Gender)
# DDSAnalyticsCf$BusinessTravel= as.numeric(DDSAnalyticsCf$BusinessTravel)
# DDSAnalyticsCf$MaritalStatus = as.numeric(DDSAnalyticsCf$MaritalStatus)
# DDSAnalyticsCf$JobRole = as.numeric(DDSAnalyticsCf$JobRole)
#
# lotistic <- glm(Attrition ~ YearsWithCurrManager + Age +YearsInCurrentRole+ YearsSinceLastPromotion + TotalWorkingYears + TrainingTimesLastYear+ WorkLifeBalance + YearsAtCompany + StockOptionLevel+PercentSalaryHike+ PerformanceRating+RelationshipSatisfaction+ NumCompaniesWorked+MonthlyIncome+ MonthlyRate+JobSatisfaction+JobInvolvement+HourlyRate+EnvironmentSatisfaction+DistanceFromHome+DailyRate+Gender+
# +JobLevel+ Education+MaritalStatus+EducationField+BusinessTravel+Department+JobRole, data = DDSAnalyticsCf, family = "binomial")
# summary(lotistic)
NaiveBayes Classification
DDSAnalyticsNoAttrition <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv", sep=",",header = TRUE)
DDSAnalyticsCf <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
# using naive Bayes on original data
set.seed(7)
ddsObs = sample(seq(1,dim(DDSAnalyticsCf)[1]),round(.75*dim(DDSAnalyticsCf)[1]),replace = FALSE)
ddsTrain = DDSAnalyticsCf[ddsObs,]
dim(ddsTrain)
## [1] 652 36
ddsTest = DDSAnalyticsCf[-ddsObs,]
dim(ddsTest)
## [1] 218 36
Navmodel1= naiveBayes(ddsTrain[,-c(1,3,23,10,11,17)],ddsTrain$Attrition)
#Navmodel1= naiveBayes(ddsTrain[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)],ddsTrain$Attrition)
table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition)
##
## No Yes
## No 172 7
## Yes 17 22
#table(predict(Navmodel1,ddsTest[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)]),ddsTest$Attrition)
CM1= caret::confusionMatrix(table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition))
#CM1= confusionMatrix(table(predict(Navmodel1,ddsTest[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)]),ddsTest$Attrition))
CM1
## Confusion Matrix and Statistics
##
##
## No Yes
## No 172 7
## Yes 17 22
##
## Accuracy : 0.8899
## 95% CI : (0.8406, 0.9282)
## No Information Rate : 0.867
## P-Value [Acc > NIR] : 0.18590
##
## Kappa : 0.5835
##
## Mcnemar's Test P-Value : 0.06619
##
## Sensitivity : 0.9101
## Specificity : 0.7586
## Pos Pred Value : 0.9609
## Neg Pred Value : 0.5641
## Prevalence : 0.8670
## Detection Rate : 0.7890
## Detection Prevalence : 0.8211
## Balanced Accuracy : 0.8343
##
## 'Positive' Class : No
##
# Prediction
navPred <- predict(Navmodel1,DDSAnalyticsNoAttrition)
library(modelr)
predictionDataFrame <- add_predictions(DDSAnalyticsNoAttrition,Navmodel1,var="navPred",type = NULL)
navClassification <- predictionDataFrame[,c(1,36)]
write.csv(navClassification,"NbAttritionClassification.csv")
# Donut chart Nab Classification on DDSAnalyticsNoAttrition data set
nbClassification <- navClassification%>%group_by(navPred)%>%
dplyr::summarize(count=n(),
NavPredPercent = n()/nrow(navClassification))
nbClassificationDonut <- ggplot(data = nbClassification,aes(x=3,y=NavPredPercent,fill=navPred))+
geom_col(color="white")+
scale_fill_manual(values = c("#1E90FF", "#DC143C"))+
coord_polar("y",start = 1)+
geom_text(aes(label=paste0(round(NavPredPercent*100),"%")),
position = position_stack(vjust = 0.5),size=I(4.5),color="white")+
theme(panel.background = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.text = element_text(size=I(13)),
plot.title = element_text(hjust = 0.5,size = I(17)))+
ggtitle("naiveBayes Classification of Attrition")+
xlim(0.1,3.5)
nbClassificationDonut + labs(fill="Attrition") # change the legend title to Attrition from default title
# 200 iteration on Training / Test split Percentage to calculate accuracy
iterations = 200
masterAcc = matrix(nrow=iterations)
splitPerc = 0.75 #Training / Test split Percentage
for(j in 1:iterations){
ddsObs = sample(seq(1,dim(DDSAnalyticsCf)[1]),round(splitPerc*dim(DDSAnalyticsCf)[1]))
ddsTrain = DDSAnalyticsCf[ddsObs,]
ddsTest = DDSAnalyticsCf[-ddsObs,]
Navmodel1= naiveBayes(ddsTrain[,-c(1,3,23,10,11,17)],ddsTrain$Attrition,laplace = 1)
table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition)
CM1= caret::confusionMatrix(table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition))
masterAcc[j] = CM1$overall[1]
}
MeanAcc = colMeans(masterAcc)
MeanAcc
## [1] 0.832844
#logistic regression
#ddsPredictionData <- read.csv(file.choose())
ddsPredictionData<- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv",sep = ",",header = TRUE)
DDSAnalyticsLogistic <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
DDSAnalyticsLogistic$OverTime = as.factor(DDSAnalyticsLogistic$OverTime)
set.seed(7)
ddsObslogis = sample(seq(1,dim(DDSAnalyticsLogistic)[1]),round(.75*dim(DDSAnalyticsLogistic)[1]),replace = FALSE)
ddsLogisTrain = DDSAnalyticsLogistic[ddsObslogis,]
dim(ddsLogisTrain)
## [1] 652 36
ddsLogisTest = DDSAnalyticsLogistic[-ddsObslogis,]
dim(ddsLogisTest)
## [1] 218 36
logisModClf <- glm(Attrition ~ JobInvolvement+JobLevel+JobRole+JobSatisfaction+MaritalStatus+MonthlyIncome+MonthlyRate+
OverTime+StockOptionLevel+YearsAtCompany+YearsSinceLastPromotion+
YearsWithCurrManager+YearsInCurrentRole,
data = ddsLogisTrain, family=binomial(link="logit"))
summary(logisModClf)
##
## Call:
## glm(formula = Attrition ~ JobInvolvement + JobLevel + JobRole +
## JobSatisfaction + MaritalStatus + MonthlyIncome + MonthlyRate +
## OverTime + StockOptionLevel + YearsAtCompany + YearsSinceLastPromotion +
## YearsWithCurrManager + YearsInCurrentRole, family = binomial(link = "logit"),
## data = ddsLogisTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.92289 -0.55313 -0.27780 -0.09874 2.97668
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.441e-01 1.109e+00 0.310 0.756246
## JobInvolvement -7.887e-01 1.789e-01 -4.409 1.04e-05 ***
## JobLevel -3.760e-01 4.482e-01 -0.839 0.401495
## JobRoleHuman Resources 1.101e+00 8.326e-01 1.323 0.185906
## JobRoleLaboratory Technician 1.010e+00 6.758e-01 1.494 0.135213
## JobRoleManager 1.932e-01 1.062e+00 0.182 0.855658
## JobRoleManufacturing Director -1.236e+00 9.205e-01 -1.343 0.179254
## JobRoleResearch Director -1.360e+00 1.402e+00 -0.970 0.332129
## JobRoleResearch Scientist 3.898e-01 6.750e-01 0.577 0.563617
## JobRoleSales Executive 8.252e-01 5.868e-01 1.406 0.159653
## JobRoleSales Representative 1.499e+00 7.438e-01 2.016 0.043838 *
## JobSatisfaction -5.026e-01 1.185e-01 -4.242 2.22e-05 ***
## MaritalStatusMarried 1.379e+00 4.423e-01 3.118 0.001822 **
## MaritalStatusSingle 1.876e+00 5.375e-01 3.490 0.000483 ***
## MonthlyIncome 4.877e-05 1.187e-04 0.411 0.681311
## MonthlyRate -1.439e-05 1.810e-05 -0.795 0.426539
## OverTimeYes 1.797e+00 2.677e-01 6.713 1.91e-11 ***
## StockOptionLevel -1.044e-01 2.012e-01 -0.519 0.603812
## YearsAtCompany 1.131e-02 4.633e-02 0.244 0.807129
## YearsSinceLastPromotion 2.335e-01 5.887e-02 3.965 7.33e-05 ***
## YearsWithCurrManager -1.428e-01 6.184e-02 -2.309 0.020924 *
## YearsInCurrentRole -1.570e-01 6.332e-02 -2.479 0.013176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 594.98 on 651 degrees of freedom
## Residual deviance: 416.65 on 630 degrees of freedom
## AIC: 460.65
##
## Number of Fisher Scoring iterations: 6
varImp(logisModClf) # displays all the important variables
## Overall
## JobInvolvement 4.4092415
## JobLevel 0.8389543
## JobRoleHuman Resources 1.3227870
## JobRoleLaboratory Technician 1.4938581
## JobRoleManager 0.1819037
## JobRoleManufacturing Director 1.3430570
## JobRoleResearch Director 0.9698350
## JobRoleResearch Scientist 0.5774769
## JobRoleSales Executive 1.4062395
## JobRoleSales Representative 2.0156343
## JobSatisfaction 4.2418899
## MaritalStatusMarried 3.1178152
## MaritalStatusSingle 3.4900623
## MonthlyIncome 0.4106743
## MonthlyRate 0.7951278
## OverTimeYes 6.7125276
## StockOptionLevel 0.5189261
## YearsAtCompany 0.2441314
## YearsSinceLastPromotion 3.9653132
## YearsWithCurrManager 2.3093618
## YearsInCurrentRole 2.4789736
pred = predict(logisModClf, newdata=ddsLogisTest,type = "response")
table(ddsLogisTest$Attrition,pred > 0.5)
##
## FALSE TRUE
## No 181 8
## Yes 13 16
ddsPredictionData$OverTime=as.factor(ddsPredictionData$OverTime)
predLogistic <- predict(logisModClf,newdata = ddsPredictionData,type="response")
# Accuracy = 90.3%
# Sensitivity = 93.3%
# Specificity = 66.666%
#https://www.shrm.org/hr-today/trends-and-forecasting/special-reports-and-expert-views/Documents/Retaining-Talent.pdf
# Turnover Analysis
# How Many Are Leaving?
# Who Is Leaving?
# Costs and Benefits?
#What causes turnover?
# satisfactry pay, good working condition and development opportunites.
# low job satisfaction
# poor performance
# more attractive than his or her current role
# work environment
# what make employee stay?
# positive work environment,
# promotional opportunities, status
# in the community,
lotisticCF <- glm(Attrition ~ Age+EnvironmentSatisfaction+JobInvolvement+JobSatisfaction+MaritalStatus+
RelationshipSatisfaction+TrainingTimesLastYear+WorkLifeBalance+YearsSinceLastPromotion+
JobRole+ MonthlyIncome+MonthlyRate+PerformanceRating+StandardHours+StockOptionLevel,
data = DDSAnalyticsCf, family = "binomial")
summary(lotisticCF)
##
## Call:
## glm(formula = Attrition ~ Age + EnvironmentSatisfaction + JobInvolvement +
## JobSatisfaction + MaritalStatus + RelationshipSatisfaction +
## TrainingTimesLastYear + WorkLifeBalance + YearsSinceLastPromotion +
## JobRole + MonthlyIncome + MonthlyRate + PerformanceRating +
## StandardHours + StockOptionLevel, family = "binomial", data = DDSAnalyticsCf)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9929 -0.5836 -0.3547 -0.1711 2.8553
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.839e+00 1.440e+00 1.971 0.048678 *
## Age -2.821e-02 1.375e-02 -2.051 0.040288 *
## EnvironmentSatisfaction -1.808e-01 9.462e-02 -1.911 0.056022 .
## JobInvolvement -7.043e-01 1.423e-01 -4.949 7.47e-07 ***
## JobSatisfaction -3.869e-01 9.386e-02 -4.122 3.76e-05 ***
## MaritalStatusMarried 9.284e-01 3.698e-01 2.510 0.012060 *
## MaritalStatusSingle 1.570e+00 4.555e-01 3.447 0.000568 ***
## RelationshipSatisfaction -1.107e-01 9.248e-02 -1.197 0.231314
## TrainingTimesLastYear -2.597e-01 8.883e-02 -2.924 0.003457 **
## WorkLifeBalance -4.047e-01 1.434e-01 -2.823 0.004754 **
## YearsSinceLastPromotion 7.122e-02 3.624e-02 1.965 0.049378 *
## JobRoleHuman Resources 9.603e-01 6.963e-01 1.379 0.167854
## JobRoleLaboratory Technician 6.490e-01 5.167e-01 1.256 0.209127
## JobRoleManager -1.399e-01 9.133e-01 -0.153 0.878246
## JobRoleManufacturing Director -1.590e+00 8.296e-01 -1.916 0.055324 .
## JobRoleResearch Director -1.298e+00 1.204e+00 -1.078 0.281082
## JobRoleResearch Scientist 5.475e-01 5.146e-01 1.064 0.287317
## JobRoleSales Executive 4.580e-01 4.532e-01 1.011 0.312170
## JobRoleSales Representative 1.798e+00 5.747e-01 3.129 0.001755 **
## MonthlyIncome -2.395e-05 6.408e-05 -0.374 0.708549
## MonthlyRate -9.877e-06 1.488e-05 -0.664 0.506750
## PerformanceRating 1.868e-01 2.857e-01 0.654 0.513240
## StandardHours NA NA NA NA
## StockOptionLevel -9.890e-03 1.823e-01 -0.054 0.956730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 767.67 on 869 degrees of freedom
## Residual deviance: 608.81 on 847 degrees of freedom
## AIC: 654.81
##
## Number of Fisher Scoring iterations: 6
In nutshell, company is always going to have attrition, some are voluntary and some involuntary. The most important thing we are focus on this project is to find out a way to retain employee as much as possible. Voluntary attrition such as retirement, personal health, relocation etc. can be very costly to the company. On the other hand, involuntary attrition such as layoffs or reductions in force and terminating poorly performing employees can sometime be beneficial as it could create an opportunity to hire high performance and motivated employee that can better much a job role. While some of the factors that is analyzed in this project are not so easy to control attrition, there are some factors such as over time, stock option and job involvement that I believe can be controlled to retain the talented employees. Reducing overtime whenever its possible or spreading hours equally among several employee could be a solution to elevate the motivation and the performance of employee. As we all like to get appreciated and get involve in company and get noticed often, I am certain that focusing on improving the job involvement could be an excellent step to take to keep attrition in balance.